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Henry, Sam, and Bridget T. McInnes. "Literature Based Discovery: Models, methods, and trends." Journal of Biomedical Informatics 74 (October 2017): 20–32. http://dx.doi.org/10.1016/j.jbi.2017.08.011.
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Cohen, T., D. Widdows, C. Stephan, R. Zinner, J. Kim, T. Rindflesch, and P. Davies. "Predicting High-Throughput Screening Results With Scalable Literature-Based Discovery Methods." CPT: Pharmacometrics & Systems Pharmacology 3, no. 10 (October 2014): 140. http://dx.doi.org/10.1038/psp.2014.37.
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Preiss, Judita, Mark Stevenson, and Robert Gaizauskas. "Exploring relation types for literature-based discovery." Journal of the American Medical Informatics Association 22, no. 5 (May 12, 2015): 987–92. http://dx.doi.org/10.1093/jamia/ocv002.
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Abstract Objective Literature-based discovery (LBD) aims to identify “hidden knowledge” in the medical literature by: (1) analyzing documents to identify pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between concepts, for example, document co-occurrence. These generate huge numbers of hypotheses, difficult for humans to assess. More complex techniques rely on linguistic analysis, for example, shallow parsing, to identify semantically stronger relations. Such approaches generate fewer hypotheses, but may miss hidden knowledge. The authors investigate this trade-off in detail, comparing techniques for identifying related concepts to discover which are most suitable for LBD. Materials and methods A generic LBD system that can utilize a range of relation types was developed. Experiments were carried out comparing a number of techniques for identifying relations. Two approaches were used for evaluation: replication of existing discoveries and the “time slicing” approach.1 Results Previous LBD discoveries could be replicated using relations based either on document co-occurrence or linguistic analysis. Using relations based on linguistic analysis generated many fewer hypotheses, but a significantly greater proportion of them were candidates for hidden knowledge. Discussion and Conclusion The use of linguistic analysis-based relations improves accuracy of LBD without overly damaging coverage. LBD systems often generate huge numbers of hypotheses, which are infeasible to manually review. Improving their accuracy has the potential to make these systems significantly more usable.
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Maurel, Denis, Sandy Chéry, Nicole Bidoit, Philippe Chatalic, Aziza Filali, Christine Froidevaux, and Anne Poupon. "Transducer Cascades for Biological Literature-Based Discovery." Information 13, no. 5 (May 20, 2022): 262. http://dx.doi.org/10.3390/info13050262.
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G protein-coupled receptors (GPCRs) control the response of cells to many signals, and as such, are involved in most cellular processes. As membrane receptors, they are accessible at the surface of the cell. GPCRs are also the largest family of membrane receptors, with more than 800 representatives in mammal genomes. For this reason, they are ideal targets for drugs. Although about one third of approved drugs target GPCRs, only about 16% of GPCRs are targeted by drugs. One of the difficulties comes from the lack of knowledge on the intra-cellular events triggered by these molecules. In the last two decades, scientists have started mapping the signaling networks triggered by GPCRs. However, it soon appeared that the system is very complex, which led to the publication of more than 320,000 scientific papers. Clearly, a human cannot take into account such massive sources of information. These papers represent a mine of information about both ontological knowledge and experimental results related to GPCRs, which have to be exploited in order to build signaling networks. The ABLISS project aims at the automatic building of GPCRs networks using automated deductive reasoning, allowing to integrate all available data. Therefore, we processed the automatic extraction of network information from the literature using Natural Language Processing (NLP). We mainly focused on the experimental results about GPCRs reported in the scientific papers, as so far there is no source gathering all these experimental results. We designed a relational database in order to make them available to the scientific community later. After introducing the more general objectives of the ABLISS project, we describe the formalism in detail. We then explain the NLP program using the finite state methods (Unitex graph cascades) we implemented and discuss the extracted facts obtained. Finally, we present the design of the relational database that stores the facts extracted from the selected papers.
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Vázquez, Javier, Manel López, Enric Gibert, Enric Herrero, and F. Javier Luque. "Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches." Molecules 25, no. 20 (October 15, 2020): 4723. http://dx.doi.org/10.3390/molecules25204723.
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Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.
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Srinivasan, Mythily, Corinne Blackburn, Mohamed Mohamed, A. V. Sivagami, and Janice Blum. "Literature–Based Discovery of Salivary Biomarkers for Type 2 Diabetes Mellitus." Biomarker Insights 10 (January 2015): BMI.S22177. http://dx.doi.org/10.4137/bmi.s22177.
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The alarming increase in type 2 diabetes mellitus (T2DM) underscores the need for efficient screening and preventive strategies. Select protein biomarker profiles emerge over time during T2DM development. Periodic evaluation of these markers will increase the predictive ability of diabetes risk scores. Noninvasive methods for frequent measurements of biomarkers are increasingly being investigated. Application of salivary diagnostics has gained importance with the establishment of significant similarities between the salivary and serum proteomes. The objective of this study is to identify T2DM–specific salivary biomarkers by literature–based discovery. A serial interrogation of the PubMed database was performed using MeSH terms of specific T2DM pathological processes in primary and secondary iterations to compile cohorts of T2DM–specific serum markers. Subsequent search consisted of mining for the identified serum markers in human saliva. More than 60% of T2DM–associated serum proteins have been measured in saliva. Nearly half of these proteins have been reported in diabetic saliva. Measurements of salivary lipids and oxidative stress markers that can exhibit correlated saliva plasma ratio could constitute reliable factors for T2DM risk assessment. We conclude that a high percentage of T2DM–associated serum proteins can be measured in saliva, which offers an attractive and economical strategy for T2DM screening.
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Özgür, Arzucan, Zuoshuang Xiang, Dragomir R. Radev, and Yongqun He. "Literature-Based Discovery of IFN-γand Vaccine-Mediated Gene Interaction Networks." Journal of Biomedicine and Biotechnology 2010 (2010): 1–13. http://dx.doi.org/10.1155/2010/426479.
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Interferon-gamma (IFN-γ) regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-γ-related gene interaction network from a large amount of IFN-γresearch reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP) and network centrality analysis. The interaction network of human IFN-γ(Gene symbol: IFNG) and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-γrelatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.
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Lou, Pei, An Fang, Wanqing Zhao, Kuanda Yao, Yusheng Yang, and Jiahui Hu. "Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph–Based Approach." Journal of Medical Internet Research 25 (October 20, 2023): e45225. http://dx.doi.org/10.2196/45225.
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Background The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. Objective The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph–based approach. Methods We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. Results The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed. Conclusions We showed the effectiveness of a knowledge graph–based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.
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Sadoughi, Fatemeh, Jamal Hallajzadeh, Zatollah Asemi, Mohammad A. Mansournia, and Bahman Yousefi. "Nanocellulose-based Delivery Systems and Cervical Cancer: Review of the Literature." Current Pharmaceutical Design 27, no. 46 (December 2021): 4707–15. http://dx.doi.org/10.2174/1381612827666210927110937.
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It has become clear that targeted therapy is one of the best options for decreasing the unpleasant side effects of existing common methods and reducing the number of deaths occurred due to many types of cancer. Biocompatible and non-toxic delivery systems are provided by nanomedicine for aiding targeted therapy in many diseases containing cancer. Cervical cancer (CC) is not only the most common gynecological cancer but also is ranked as the fourth common cancer between both men and women. Chemotherapy, radiotherapy, surgery, and immunotherapy are the approaches, which are being used for treating CC patients. However, more efficacy of these methods can be achieved with the help of nanomedicine and novel delivery systems. Nanocellulose is one of the agents used for designing these systems in order to deliver different drugs to a diversity of cancerous cells. In this review, we aim to investigate the competency of nanocellulose for establishing novel therapeutic methods for cervical cancer. We hope that our results help develop more drug delivery systems for targeted therapy to reduce the side effects and induce the efficacy of anti-cancer drugs.
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De Bonis, Michele, Fabrizio Falchi, and Paolo Manghi. "Graph-based methods for Author Name Disambiguation: a survey." PeerJ Computer Science 9 (September 11, 2023): e1536. http://dx.doi.org/10.7717/peerj-cs.1536.
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Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers’ activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic.
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Du, Jian, and Xiaoying Li. "A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development." JMIR Medical Informatics 8, no. 4 (April 28, 2020): e18323. http://dx.doi.org/10.2196/18323.
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Background Combination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders. Objective This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines. Methods Based on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S1-P-O and Si-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, “treat”) and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as “pharmacologic actions” and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation. Results We retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching “antineoplastic agents” for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms “conclusion*” and “conclude*” ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers (“combin*,” “coadministration,” “co-administered,” and “regimen”) to identify potential combination therapies to enable development of a machine learning algorithm. Conclusions Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.
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Brown, Adam S., and Chirag J. Patel. "MeSHDD: Literature-based drug-drug similarity for drug repositioning." Journal of the American Medical Informatics Association 24, no. 3 (September 27, 2016): 614–18. http://dx.doi.org/10.1093/jamia/ocw142.
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Objective: Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms. Methods: We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs. Results We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin. Conclusion: Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/).
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Yen, Y. T., B. Chen, H. W. Chiu, Y. C. Lee, Y. C. Li, and C. Y. Hsu. "Developing an NLP and IR-based Algorithm for Analyzing Gene-disease Relationships." Methods of Information in Medicine 45, no. 03 (2006): 321–29. http://dx.doi.org/10.1055/s-0038-1634069.
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Summary Objectives: High-throughput techniques such as cDNA microarray, oligonucleotide arrays, and serial analysis of gene expression (SAGE) have been developed and used to automatically screen huge amounts of gene expression data. However, researchers usually spend lots of time and money on discovering gene-disease relationships by utilizing these techniques. We prototypically implemented an algorithm that can provide some kind of predicted results for biological researchers before they proceed with experiments, and it is very helpful for them to discover gene-disease relationships more efficiently. Methods: Due to the fast development of computer technology, many information retrieval techniques have been applied to analyze huge digital biomedical databases available worldwide. Therefore we highly expect that we can apply information retrieval (IR) technique to extract useful information for the relationship of specific diseases and genes from MEDLINE articles. Furthermore, we also applied natural language processing (NLP) methods to do the semantic analysis for the relevant articles to discover the relationships between genes and diseases. Results: We have extracted gene symbols from our literature collection according to disease MeSH classifications. We have also built an IR-based retrieval system, “Biomedical Literature Retrieval System (BLRS)“ and applied the N-gram model to extract the relationship features which can reveal the relationship between genes and diseases. Finally, a relationship network of a specific disease has been built to represent the gene-disease relationships. Conclusions: A relationship feature is a functional word that can reveal the relationship between one single gene and a disease. By incorporating many modern IR techniques, we found that BLRS is a very powerful information discovery tool for literature searching. A relationship network which contains the information on gene symbol, relationship feature, and disease MeSH term can provide an integrated view to discover gene-disease relationships.
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Yongjun Zhang. "A Recommender System for Personalized Reading Recommendations and Literature Discovery utilizing the HGRNN-EOO technique." Journal of Electrical Systems 20, no. 3s (April 4, 2024): 2283–96. http://dx.doi.org/10.52783/jes.3053.
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Recommender systems are used to address information overload, enhance personalization, and improve user experience by providing tailored suggestions based on individual preferences, thereby increasing engagement and facilitating content discovery. This paper proposes a hybrid approach for recommender system in personalized reading recommendation and literature discovery. The proposed hybrid approach is the combined performance of both the Hierarchal Gated Recurrent Neural Network (HGRNN) and Eurasian Oystercatcher Optimizer (EOO).Commonly it is named as HGRNN-EOO technique. The major objective of the proposed approach is to provide a recommender system for personalized reading recommendation and literature discovery. HGRNN is designed to provide personalized recommendations based on their preferences, behaviour, and interactions to enhance user experience and engagement. The personalized recommendations from the HGRNN are optimized by using the EOO. By then, the MATLAB working platform has been proposed and implemented, and the present processes are used to calculate the execution. Using performance metrics like accuracy, error rate, F-score, precision, recall, computation time, ROC, sensitivity, and specificity, the proposed method's effectiveness is evaluated. From the result, the proposed approach based error is less compared to existing techniques. The result shows that the accuracy level of proposed Recommender System in Personalized Reading Recommendation using Hierarchal Gated Recurrent Neural Network and Eurasian Oystercatcher Optimizer (RSPRR-HGRNN-EOO) approach is 98% that is higher than the other existing methods. The specificity and the F-score of the proposed RSPRR-HGRNN-EOO approach is 99% and 97%. The error rate of the proposed RSPRR-HGRNN-EOO approach is 2.5%, which is very less compared to other existing techniques. The proposed method shows better results in all existing methods like Recommender System in Personalized Reading Recommendation Convolutional Neural Network (RSPRR-CNN), Recommender System in Personalized Reading Recommendation Deep Neural Network (RSPRR-DNN) and Recommender System in Personalized Reading Recommendation Feed-Forward Neural Network (RSPRR-FNN). Based on the outcome, it can be concluded that the proposed strategy has a lower error rate than existing methods.
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Borges, Heraldo, Murillo Dutra, Amin Bazaz, Rafaelli Coutinho, Fábio Perosi, Fábio Porto, Florent Masseglia, Esther Pacitti, and Eduardo Ogasawara. "Spatial-time motifs discovery." Intelligent Data Analysis 24, no. 5 (September 30, 2020): 1121–40. http://dx.doi.org/10.3233/ida-194759.
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Discovering motifs in time series data has been widely explored. Various techniques have been developed to tackle this problem. However, when it comes to spatial-time series, a clear gap can be observed according to the literature review. This paper tackles such a gap by presenting an approach to discover and rank motifs in spatial-time series, denominated Combined Series Approach (CSA). CSA is based on partitioning the spatial-time series into blocks. Inside each block, subsequences of spatial-time series are combined in a way that hash-based motif discovery algorithm is applied. Motifs are validated according to both temporal and spatial constraints. Later, motifs are ranked according to their entropy, the number of occurrences, and the proximity of their occurrences. The approach was evaluated using both synthetic and seismic datasets. CSA outperforms traditional methods designed only for time series. CSA was also able to prioritize motifs that were meaningful both in the context of synthetic data and also according to seismic specialists.
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Gomes, Poliana, Luiz Verçosa, Fagner Melo, Vinícius Silva, Carmelo Bastos Filho, and Byron Bezerra. "Artificial Intelligence-Based Methods for Business Processes: A Systematic Literature Review." Applied Sciences 12, no. 5 (February 23, 2022): 2314. http://dx.doi.org/10.3390/app12052314.
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Companies are usually overloaded with data that they may not know how to take advantage of. On the other hand, artificial intelligence (AI) techniques are known to “keep learning” as the data increase. In this context, our research question emerges: what AI-based methods, in the literature, could be used to automatize business processes and support the decision-making processes of companies? To fill this gap, in this paper, we performed a review of the literature to identify these techniques. We ensured the usage of methods since they allowed reproducibility and extensions. We applied our search string in the Scopus and Web of Science databases and discovered 21 relevant papers pertaining to our question. In these papers, we identified methods that automated tasks and helped analysts make assertive decisions when designing, extending, or reengineering business processes. The authors applied diverse AI techniques, such as K-means, Bayesian networks, and swarm intelligence. Our analysis provides statistics about the techniques and problems being tackled and point to possible future directions.
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Rindflesch, Thomas, Dimitar Hristovski, and Andrej Kastrin. "Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery." Methods of Information in Medicine 55, no. 04 (2016): 340–46. http://dx.doi.org/10.3414/me15-01-0108.
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Summary Objectives:Literature-based discovery (LBD) is a text mining methodology for automatically generating research hypotheses from existing knowledge. We mimic the process of LBD as a classification problem on a graph of MeSH terms. We employ unsupervised and supervised link prediction methods for predicting previously unknown connections between biomedical concepts. Methods:We evaluate the effectiveness of link prediction through a series of experiments using a MeSH network that contains the history of link formation between biomedical concepts. We performed link prediction using proximity measures, such as common neighbor (CN), Jaccard coefficient (JC), Adamic / Adar index (AA) and preferential attachment (PA). Our approach relies on the assumption that similar nodes are more likely to establish a link in the future. Results:Applying an unsupervised approach, the AA measure achieved the best performance in terms of area under the ROC curve (AUC = 0.76),gfollowed by CN, JC, and PA. In a supervised approach, we evaluate whether proximity measures can be combined to define a model of link formation across all four predictors. We applied various classifiers, including decision trees, k-nearest neighbors, logistic regression, multilayer perceptron, naïve Bayes, and random forests. Random forest classifier accomplishes the best performance (AUC = 0.87). Conclusions:The link prediction approach proved to be effective for LBD processing. Supervised statistical learning approaches clearly outperform an unsupervised approach to link prediction.
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BUSI, NADIA, and G. MICHELE PINNA. "Process discovery and Petri nets." Mathematical Structures in Computer Science 19, no. 6 (December 2009): 1091–124. http://dx.doi.org/10.1017/s0960129509990132.
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The aim of the research domain known as process mining is to use process discovery to construct a process model as an abstract representation of event logs. The goal is to build a model (in terms of a Petri net) that can reproduce the logs under consideration, and does not allow different behaviours compared with those shown in the logs. In particular, process mining aims to verify the accuracy of the model design (represented as a Petri net), basically checking whether the same net can be rediscovered. However, the main mining methods proposed in the literature have some drawbacks: the classical α-algorithm is unable to rediscover various nets, while the region-based approach, which can mine them correctly, is too complex.In this paper, we compare different approaches and propose some ideas to counter the weaknesses of the region-based approach.
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Prachagool, Veena. "Literature and Project-Based Learning and Learning Outcomes of Young Children." International Education Studies 14, no. 12 (November 26, 2021): 93. http://dx.doi.org/10.5539/ies.v14n12p93.
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In the early years, children learn by taking an action and touching opportunities which experiences the world as much as possible. It is an internal process that allows children to meaningfully reflect their experiences from abstract to further learning. Literature and project-based learning management is a learning approach that strengthens the attitude of the pursuit of knowledge, helping children to have a habit of reading, creating opportunities to leading in the discovery of something meaningful to life. The research objectives were to study learning outcomes of young children through literature and project-based learning. Twenty-five young children were studied and reported their learning outcomes. Data were collected through variety of methods: observation, debriefing focus group, and interviews after the scenario. Data were collected by qualitative and quantitative methods. The findings indicated that young children had the highest level of understanding and ability to manage literary learning and projects. They also had ability to provide the most literary and project management environment were ranges low and highest based on the different perception and potential of learning. It can be recommended literature and project-based learning is suitable for early childhood education.
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UYSAL, İLHAN, and H. ALTAY GÜVENIR. "An overview of regression techniques for knowledge discovery." Knowledge Engineering Review 14, no. 4 (December 1999): 319–40. http://dx.doi.org/10.1017/s026988899900404x.
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Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive partitioning regression methods that induce regression trees (CART, RETIS and M5).
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Putri, Shabika Azzaria, Labitha Cetizta Irwanti, and Ari Rahmat Elsad. "Legal Discovery in Islamic Perspective." UNIFIKASI : Jurnal Ilmu Hukum 8, no. 1 (June 29, 2021): 43–52. http://dx.doi.org/10.25134/unifikasi.v8i1.3848.
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In general, legal discovery refers to conducting legal searches when the statutory regulations are not regulated and are unclear. Legal discovery is not only based on the concept of positive law, but it also refers to Islamic concepts. This aims to find the law on an issue where the regulation is not yet regulated. Thus, inexistence and unclear issues in law become present and apparent. Legal discovery in Islamic concept is known as Ijtihad, an act and an effort to find, understand, and formulate Islamic Shari’ah ruling. Legal discovery in Islam is conducted using several methods including istinbat, interpretation, literal/linguistic, causation (ta’lili), and synchronization methodologies. Other methods of legal discovery are qiyas, istihsan, maslahah mursalah, istishhab, urf, mazhab shahabi which cannot be separated from the main sources of Islamic law, the Qur’an and hadith. Meanwhile, ra'yu and ijtihad are ways of thinking in understanding the Qur’an and hadith. These are to determine a problem where its nash has not been determined. The researchers employed library research in this study. The study examined the documents using secondary data and analyzed it using a qualitative method where the data are described in words, not numbers. In addition, the data collection is based on literature studies taken from books, journals, and internet sources related to legal discovery in the Islamic concept.
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Idris, Nazihah, Othman Talib, and Fazilah Razali. "Strategies in Mastering Science Process Skills in Science Experiments: A Systematic Literature Review." Jurnal Pendidikan IPA Indonesia 11, no. 1 (March 31, 2022): 155–70. http://dx.doi.org/10.15294/jpii.v11i1.32969.
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Science is a knowledge discipline that is experimentally oriented. The science experiment is one of the core activities in science learning. It is a process that prioritises methods of investigation and problem-solving where the scientific method is employed. In science experiments, mastery of scientific process skills is required. Hence, it is crucial as this will expose students to scientific methods and knowledge. This study aims to identify what are the appropriate strategies that may be employed to augment learners’ science process skills. This article conducts a systematic literature review and twenty-two articles have been chosen to be analysed. The current study combined many research designs, where the review fulfilled the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) publishing standard. Web of Science and Scopus, two well-known databases, were used to discover articles for this study. This review includes a topic based on the thematic analysis which is strategies in mastering science process skills. The results show seven sub-themes based on the topic that are 1) Hands-on and minds-on implementation 2) Inquiry-based approach 3) Discovery learning 4) Strategic manipulative skills 5) Argumentation Skills 6) Using Information and Communication Technologies and 7) Implementing Engineering-oriented science, technology, engineering, and mathematics (STEM) Integration Activities. The research’s findings may motivate science educators to use the appropriate strategies while undergoing science experiments to improve SPS which are important competencies that can influence student’s performance in science learning.
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Yazid, Afthon, and Arif Sugitanata. "The Complexity and Diversity Methods of Legal Discovery in Islam: In the Perspective Ulama of Mazhab al-Arba'ah." Kawanua International Journal of Multicultural Studies 4, no. 2 (December 31, 2023): 152–64. http://dx.doi.org/10.30984/kijms.v4i2.725.
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This article aims to detail the development of the method of finding sources of Islamic law concerning the perspectives of the scholars of the madhhab al-arba'ah, and explore its advantages and disadvantages based on the findings gathered. The research adopts a literature-based approach, a research method that relies on literature as the main source of information. Primary data sources are obtained from classical works, books, journals, and other related references related to the method of finding sources of Islamic law, especially in the perspective of the ulama mazhab al-arba'ah. Researchers use a qualitative approach with analytical descriptive methods to analyse primary data. The results show that the schools of Islamic law, such as Hanafi, Maliki, Shafi'i, and Hambali, show differences in the method of legal discovery and its implementation. Each school has distinctive characteristics based on its origin's sociocultural and political context. The thoughts of the leading figures in developing the science of usul fiqh, such as Abu Hanifah, Imam Malik, Imam Shafi'i, and Imam Ahmad, reflect the differences in ijtihad and law-making methods in these schools. Thus, this article provides an overview of the complexity and diversity of legal discovery methods in the Islamic context, illustrates the evolution of legal thought over time, and highlights the different approaches among the schools of Islamic law.
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Li, Chenhao, Jiyin Zhang, Amruta Kale, Xiang Que, Sanaz Salati, and Xiaogang Ma. "Toward Trust-Based Recommender Systems for Open Data: A Literature Review." Information 13, no. 7 (July 12, 2022): 334. http://dx.doi.org/10.3390/info13070334.
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In recent years, the concept of “open data” has received increasing attention among data providers and publishers. For some data portals in public sectors, such as data.gov, the openness enables public oversight of governmental proceedings. For many other data portals, especially those in academia, open data has shown its potential for driving new scientific discoveries and creating opportunities for multidisciplinary collaboration. While the number of open data portals and the volume of shared data have increased significantly, most open data portals still use keywords and faceted models as their primary methods for data search and discovery. There should be opportunities to incorporate more intelligent functions to facilitate the data flow between data portals and end-users. To find more theoretical and empirical evidence for that proposition, in this paper, we conduct a systematic literature review of open data, social trust, and recommender systems to explain the fundamental concepts and illustrate the potential of using trust-based recommender systems for open data portals. We hope this literature review can benefit practitioners in the field of open data and facilitate the discussion of future work.
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Orlov, Yuriy L., Ancha V. Baranova, and Tatiana V. Tatarinova. "Bioinformatics Methods in Medical Genetics and Genomics." International Journal of Molecular Sciences 21, no. 17 (August 28, 2020): 6224. http://dx.doi.org/10.3390/ijms21176224.
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Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein–protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at “Systems Biology and Bioinformatics”-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools.
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Gianfredi, Vincenza, Antonietta Filia, Maria Cristina Rota, Roberto Croci, Lorenzo Bellini, Anna Odone, and Carlo Signorelli. "Vaccine Procurement: A Conceptual Framework Based on Literature Review." Vaccines 9, no. 12 (December 3, 2021): 1434. http://dx.doi.org/10.3390/vaccines9121434.
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Ensuring timely access to affordable vaccines has been acknowledged as a global public health priority, as also recently testified by the debate sparked during the COVID-19 pandemic. Effective vaccine procurement strategies are essential to reach this goal. Nevertheless, this is still a neglected research topic. A narrative literature review on vaccine procurement was conducted, by retrieving articles from four academic databases (PubMed/MEDLINE, Scopus, Embase, WebOfScience), ‘grey’ literature reports, and institutional websites. The aim was to clarify key concepts and definitions relating to vaccine procurement, describe main vaccine procurement methods, and identify knowledge gaps and future perspectives. A theoretical conceptual framework was developed of the key factors involved in vaccine procurement, which include quality and safety of the product, forecasting and budgeting, procurement legislation, financial sustainability, and plurality of manufacture, contracting, investment in training, storage and service delivery, monitoring and evaluation. This information can be useful to support policymakers during planning, implementation, and evaluation of regional and national vaccine procurement strategies and policies.
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Llinas del Torrent, Claudia, Laura Pérez-Benito, and Gary Tresadern. "Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors." Molecules 24, no. 6 (March 20, 2019): 1098. http://dx.doi.org/10.3390/molecules24061098.
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Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.
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Arora, Kawal, Alwiyah, and Muhammad Faisal. "The Use of Data Science in Digital Marketing Techniques: Work Programs, Performance Sequences and Methods." Startupreneur Business Digital (SABDA Journal) 1, no. 2 (September 20, 2022): 143–55. http://dx.doi.org/10.34306/sabda.v1i2.110.
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Over the past decade, there have been tremendous advances in the use of data science to facilitate decision making and extract and thus act upon insights from large data sets in digital business environments. However, despite these advances, there is still a lack of relevant evidence on actions to improve data science management in digital businesses. To fill this gap in the literature. The purpose of this study is to review (i) analytical methods, (ii) usage, and performance metrics based on (iii). Data science used in digital business techniques and strategies. To this end, a comprehensive literature search was carried out on the important scientific contributions made so far in this area of research. The results provide an overview of the most important applications of data science in digital business. Generate insights related to the creation of innovative data mining and knowledge discovery techniques. Important theoretical implications are discussed and a list of topics for further research in this area is provided. This report aims to develop recommendations for enhancing digital business strategies for enterprises, marketing, and non-technical researchers, and identify directions for further research on innovative data mining and discovery applications in science.
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Arora, Kawal, Alwiyah, and Muhammad Faisal. "The Use of Data Science in Digital Marketing Techniques: Work Programs, Performance Sequences and Methods." Startupreneur Business Digital (SABDA Journal) 1, no. 2 (September 20, 2022): 143–55. http://dx.doi.org/10.33050/sabda.v1i2.110.
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Over the past decade, there have been tremendous advances in the use of data science to facilitate decision making and extract and thus act upon insights from large data sets in digital business environments. However, despite these advances, there is still a lack of relevant evidence on actions to improve data science management in digital businesses. To fill this gap in the literature. The purpose of this study is to review (i) analytical methods, (ii) usage, and performance metrics based on (iii). Data science used in digital business techniques and strategies. To this end, a comprehensive literature search was carried out on the important scientific contributions made so far in this area of research. The results provide an overview of the most important applications of data science in digital business. Generate insights related to the creation of innovative data mining and knowledge discovery techniques. Important theoretical implications are discussed and a list of topics for further research in this area is provided. This report aims to develop recommendations for enhancing digital business strategies for enterprises, marketing, and non-technical researchers, and identify directions for further research on innovative data mining and discovery applications in science.
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Thabtah, Fadi, Suhel Hammoud, and Hussein Abdel-Jaber. "Parallel Associative Classification Data Mining Frameworks Based MapReduce." Parallel Processing Letters 25, no. 02 (June 2015): 1550002. http://dx.doi.org/10.1142/s0129626415500024.
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Associative classification (AC) is a research topic that integrates association rules with classification in data mining to build classifiers. After dissemination of the Classification-based Association Rule algorithm (CBA), the majority of its successors have been developed to improve either CBA's prediction accuracy or the search for frequent ruleitems in the rule discovery step. Both of these steps require high demands in processing time and memory especially in cases of large training data sets or a low minimum support threshold value. In this paper, we overcome the problem of mining large training data sets by proposing a new learning method that repeatedly transforms data between line and item spaces to quickly discover frequent ruleitems, generate rules, subsequently rank and prune rules. This new learning method has been implemented in a parallel Map-Reduce (MR) algorithm called MRMCAR which can be considered the first parallel AC algorithm in the literature. The new learning method can be utilised in the different steps within any AC or association rule mining algorithms which scales well if contrasted with current horizontal or vertical methods. Two versions of the learning method (Weka, Hadoop) have been implemented and a number of experiments against different data sets have been conducted. The ground bases of the comparisons are classification accuracy and time required by the algorithm for data initialization, frequent ruleitems discovery, rule generation and rule pruning. The results reveal that MRMCAR is superior to both current AC mining algorithms and rule based classification algorithms in improving the classification performance with respect to accuracy.
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Zhang, Chi, Bryant Chen, and Judea Pearl. "A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10318–25. http://dx.doi.org/10.1609/aaai.v34i06.6595.
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Modern causal analysis involves two major tasks, discovery and identification. The first aims to learn a causal structure compatible with the available data, the second leverages that structure to estimate causal effects. Rather than performing the two tasks in tandem, as is usually done in the literature, we propose a symbiotic approach in which the two are performed simultaneously for mutual benefit; information gained through identification helps causal discovery and vice versa. This approach enables the usage of Verma constraints, which remain dormant in constraint-based methods of discovery, and permit us to learn more complete structures, hence identify a larger set of causal effects than previously achievable with standard methods.
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Yates, Thomas T. "29. Discovery, Integration, Communication, and Engagement: Learning Through Scaffolds in a Field-Based Course." Collected Essays on Learning and Teaching 5 (June 19, 2012): 167. http://dx.doi.org/10.22329/celt.v5i0.3442.
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A field-based course in an applied science program can have numerous learning outcomes. These are typically addressed through demonstration, active participation by the students, communication between students and instructor and amongst students, and independent work by students individually or in small groups. Such courses are also opportunities for students to develop their critical thinking. The author’s experience is that teaching techniques used to deliver field courses are generally inherent and based on the experience of the instructor and the teaching culture within the academic unit. These techniques are typically not drawn from the pedagogical literature, although they do have similarities to such established concepts such as scaffolds. Recognition of teaching concepts drawn from the pedagogical literature and their application to the design and teaching of field-based courses may improve the delivery of course material and provide a better student experience. Thinking and teaching in terms of the support that scaffolds represent may also smooth the transition from classroom to outdoors back to classroom. Supported learning based on established teaching methods will improve a student’s opportunity for Discovery, Integration, Communication and Engagement.
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Scarano, Naomi, Chiara Brullo, Francesca Musumeci, Enrico Millo, Santina Bruzzone, Silvia Schenone, and Elena Cichero. "Recent Advances in the Discovery of SIRT1/2 Inhibitors via Computational Methods: A Perspective." Pharmaceuticals 17, no. 5 (May 8, 2024): 601. http://dx.doi.org/10.3390/ph17050601.
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Sirtuins (SIRTs) are classified as class III histone deacetylases (HDACs), a family of enzymes that catalyze the removal of acetyl groups from the ε-N-acetyl lysine residues of histone proteins, thus counteracting the activity performed by histone acetyltransferares (HATs). Based on their involvement in different biological pathways, ranging from transcription to metabolism and genome stability, SIRT dysregulation was investigated in many diseases, such as cancer, neurodegenerative disorders, diabetes, and cardiovascular and autoimmune diseases. The elucidation of a consistent number of SIRT–ligand complexes helped to steer the identification of novel and more selective modulators. Due to the high diversity and quantity of the structural data thus far available, we reviewed some of the different ligands and structure-based methods that have recently been used to identify new promising SIRT1/2 modulators. The present review is structured into two sections: the first includes a comprehensive perspective of the successful computational approaches related to the discovery of SIRT1/2 inhibitors (SIRTIs); the second section deals with the most interesting SIRTIs that have recently appeared in the literature (from 2017). The data reported here are collected from different databases (SciFinder, Web of Science, Scopus, Google Scholar, and PubMed) using “SIRT”, “sirtuin”, and “sirtuin inhibitors” as keywords.
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Filik, Karolina, Bożena Szermer-Olearnik, Sabina Oleksy, Jan Brykała, and Ewa Brzozowska. "Bacteriophage Tail Proteins as a Tool for Bacterial Pathogen Recognition—A Literature Review." Antibiotics 11, no. 5 (April 21, 2022): 555. http://dx.doi.org/10.3390/antibiotics11050555.
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In recent years, a number of bacterial detection methods have been developed to replace time-consuming culture methods. One interesting approach is to mobilize the ability of phage tail proteins to recognize and bind to bacterial hosts. In this paper, the authors provide an overview of the current methodologies in which phage proteins play major roles in detecting pathogenic bacteria. Authors focus on proteins capable of recognizing highly pathogenic strains, such as Acinetobacter baumannii, Campylobacter spp., Yersinia pestis, Pseudomonas aeruginosa, Listeria monocytogenes, Staphylococcus aureus, Enterococcus spp., Salmonella spp., and Shigella. These pathogens may be diagnosed by capture-based detection methods involving the use of phage protein-coated nanoparticles, ELISA (enzyme-linked immunosorbent assay)-based methods, or biosensors. The reviewed studies show that phage proteins are becoming an important diagnostic tool due to the discovery of new phages and the increasing knowledge of understanding the specificity and functions of phage tail proteins.
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Sinha Choudhury, Ananya, Wendy Hui, and John Lau. "Using literature-based discovery to develop hypotheses for the moderating effect of massively multiplayer online games." F1000Research 12 (January 13, 2023): 53. http://dx.doi.org/10.12688/f1000research.128841.1.
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Background: Empirical studies have shown that the relationship between psychological flow state and game addiction tends to be weaker in massively multiplayer online (MMO) games compared with non-MMO games. However, a theoretical explanation for the moderating effect of MMO games is lacking in the literature. This paper uses interview data and a method for generating hypotheses, literature-based discovery (LBD), to identify potential moderating factors and develop theories about this relationship. Methods: The proposed method involved text mining 2,829 abstracts to generate a keyword list of potential underlying moderating factors. Interview data from three domain experts confirmed the usefulness of LBD. Instead of arriving at game addiction primarily through flow, the interview data revealed that different cognitive pathways may lead to game addiction in MMO games. Results: Specifically, the identified keywords led to three explanations for the observed moderating effect: (1) social interaction in MMOGs may prevent the progression from flow to game addiction or induce positive peer influence; (2) game performance typically measured using a score- or point-based system in non-MMO games offers an extrinsic motivation that is more in line with flow theory; and (3) intrinsic motivation and escapism may be more important drivers of MMO game addiction. This paper summarizes the domain experts’ views on the usefulness of LBD in theory development. Conclusions: This paper uses literature-based discovery (LBD) to demonstrate how the pathways to game addiction in MMO games differ from non-MMO games. LBD is a method for generating hypotheses seldom used in the social science literature.
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Mahyuddin Syaifulloh, Sifak Indana, and Rudiana Agustini. "Profile of the Implementation of Discovey Learning Model in Science Learning." IJORER : International Journal of Recent Educational Research 3, no. 1 (January 30, 2022): 71–87. http://dx.doi.org/10.46245/ijorer.v3i1.187.
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Research was conducted to describe and analyze the implementation of discovery learning model of science learning in Indonesia. This research method uses qualitative methods with using secondary data. The sample in this research is 30 articles published in international and national journals. Based on the analysis of 30 articles on science learning using discovery learning model in Indonesia, it can be seen that the discovery learning model has a positive impact on student learning outcomes, it can improve students' critical thinking skills, students' science process skills, students' scientific literacy, aspects of problem solving skills, and also students' understanding of concepts. there are also several disadvantages of the discovery learning model, there are; it will be optimal if it combine with media or other methods, students will be confused if they do not get the instruction from the teacher, and it require a lot of preparation and learning duration. Based on the literature review of the implementation of the discovery learning model in 2012-2021 that has been carried out, it can be concluded that the discovery learning model has a positive impact on science learning.
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Pandita, Hita, and Gendoet Hartono. "Identification and Stratiraphic Position of Mollusk Type Locality at West Progo Stage." Journal of Geoscience, Engineering, Environment, and Technology 4, no. 2 (June 30, 2019): 76. http://dx.doi.org/10.25299/jgeet.2019.4.2.2682.
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The location of the discovery of mollusk fossils on the island of Java is spread in various places. One location is in the Kulon Progo region known as West Progo beds. However, due to the lack of studies of mollusk fossils in the Kulon Progo region, this resulted in a lack of understanding of the location of the discovery. This study was intended to re-record the location of fossil molluscs discovery in the Kulon Progo region, with the aim of contributing to the stratigraphic arrangement in Kulon Progo. Research methods include literature studies, field investigations and laboratory analysis. The literature study includes libraries of the Dutch colonial era regarding the location of the discovery of mollusk fossils. Field studies in the form of stratigraphic measurements and sampling. Laboratory investigations include petrographic observations and identification of micro and macro fossils. The results of the investigation successfully re-identified the Kembang Sokkoh and Spolong locations which are two types of locations on the West Progo beds. Based on the lithological characteristics of the two locations included in the Jonggrangan Formation, with the Lower Miocene age based on an analysis of the fossil content of the molluscs.
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Wanat, Karolina, Elżbieta Brzezińska, and Anna W. Sobańska. "Aspects of Drug-Protein Binding and Methods of Analyzing the Phenomenon." Current Pharmaceutical Design 24, no. 25 (November 8, 2018): 2974–85. http://dx.doi.org/10.2174/1381612824666180808145320.
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In recent decades, drug-protein interactions have been widely studied and several methods of analysis of these phenomena have been developed and improved. These can be classified into separation, physical, chromatographic and electrophoretic methods. This review depicts the assumptions and mechanisms of methods from each group, details their strengths and weaknesses, and presents examples of their usage from the literature. Equilibrium dialysis, ultrafiltration, Hummel-Dreyer method or high performance affinity chromatography are given as representative examples, but this issue is far more expanded. Nowadays, increasing attention is paid to the computational methods and molecular modeling which are convenient tools to estimate protein binding affinity based on the physicochemical properties of compounds. To gain a broader overview, the study also examines the protein binding ability and pharmacotherapy of drugs against a range of substrates such as plasma, skin, tissue and human milk.
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Bell, Emilia C. "Values-Based Practice in EBLIP: A Review." Evidence Based Library and Information Practice 17, no. 3 (September 19, 2022): 119–34. http://dx.doi.org/10.18438/eblip30176.
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Objective – This narrative literature review examines how values and a values-based practice framework are positioned as significant to evidence based practice in libraries. This includes examining the partnership between values and evidence in decision making and reflective practice. The review responds to a gap in the literature on the origins and application of values-based practice in evidence based library and information practice (EBLIP). Methods – Searches for this narrative review were conducted in library and information science databases, discovery tools, and individual journals. Forward and backward citation searches were also undertaken. Searches aimed to encompass both the EBLIP and library assessment literature. Research and professional publications were considered for inclusion based on their engagement with values and values-based practice in EBLIP processes and decisions. Results – The findings highlight how values reflect positionality, driving action and decision making in all stages of evidence based practice in libraries. The literature emphasizes the role of values when practitioners engage with critical reflective practice or invite user voices in evidence. An explicit values-based practice approach was evident in the library assessment literature, though not explicitly addressed in the EBLIP literature or EBLIP models. This is despite a partnership between evidence based practice and values-based practice in the health sciences literature, with literature on person-centred approaches aiming to relate evidence to individuals. Conclusions – The EBLIP literature could further examine how values reflect positionality and drive action and decision making across all stages of evidence based practice. Values-based practice offers an opportunity to critically reflect on whose voices, perspectives, and values are reflected in and contribute to the library and information science evidence base.
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Baek, Kyung-Wan, Kung Ahn, Yong Ju Ahn, Ying-Ying Xiang, and Ji-Seok Kim. "Exercise and Gut Microbiome: Trends and Advances in Research Methods." Exercise Science 31, no. 4 (November 30, 2022): 428–37. http://dx.doi.org/10.15857/ksep.2022.00479.
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PURPOSE: To suggest future research directions and current research trends based on representative studies of existing exercises and the gut microbiome. In addition, we reviewed methodologies to study the relationship between exercise and the gut microbiome.METHODS: The research methodologies and results were integrated through a literature review of the latest “exercise and gut microbiome” studies and a narrative review.RESULTS: Although exercise is indirectly related to the gut microbiome or immunity, evidence for a direct effect is still lacking. However, with the recent discovery of gut microbiomes that can help improve exercise performance, it is clear that exercise can positively alter the gut microbiome.CONCLUSIONS: Strong evidence suggests that regular moderate exercise improves overall immune function and lowers the incidence of inflammation-related disease. In addition, certain microorganisms affect exercise performance.
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Li, Jing, Xianqing He, Yuanyuan Deng, and Chenxi Yang. "An Update on Isolation Methods for Proteomic Studies of Extracellular Vesicles in Biofluids." Molecules 24, no. 19 (September 27, 2019): 3516. http://dx.doi.org/10.3390/molecules24193516.
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Extracellular vesicles (EVs) are lipid bilayer enclosed particles which present in almost all types of biofluids and contain specific proteins, lipids, and RNA. Increasing evidence has demonstrated the tremendous clinical potential of EVs as diagnostic and therapeutic tools, especially in biofluids, since they can be detected without invasive surgery. With the advanced mass spectrometry (MS), it is possible to decipher the protein content of EVs under different physiological and pathological conditions. Therefore, MS-based EV proteomic studies have grown rapidly in the past decade for biomarker discovery. This review focuses on the studies that isolate EVs from different biofluids and contain MS-based proteomic analysis. Literature published in the past decade (2009.1–2019.7) were selected and summarized with emphasis on isolation methods of EVs and MS analysis strategies, with the aim to give an overview of MS-based EV proteomic studies and provide a reference for future research.
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Winarti, Wahyu Tri, Hadma Yuliani, Mukhlis Rohmadi, and Nurul Septiana. "Pembelajaran Fisika Menggunakan Model Discovery Learning Berbasis Edutainment." Jurnal Ilmiah Pendidikan Fisika 5, no. 1 (February 28, 2021): 47. http://dx.doi.org/10.20527/jipf.v5i1.2789.
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Pembelajaran fisika merupakan pembelajaran mengenai gejala-gejala alam dan berusaha menemukan hubungan antara kenyataan di alam. Perlu adanya model dan metode yang dapat membantu menerangkan gejala alam tersebut dalam proses pembelajaran fisika. Salah satu model yang diharapkan cocok dengan karakteristik pembelajaran fisika yaitu model discovery learning berbasis edutainment. Penelitian ini bertujuan: (1) untuk mendeskripsikan kelebihan dan kekurangan dari model discovery learning berbasis edutainment; dan (2) untuk mendeskripsikan penerapani model discovery learning berbasis edutainment dalam pembelajaran fisika. Metode pengumpulan data pada penelitian ini adalah studi literatur dan kajian pustaka, data penelitian ini menggunakan jenis data sekunder, kemudian data yang diperoleh tersebut diolah dengan analisis isi. Hasil penelitian menunjukkan bahwa: (1) Model discovery learning berbasis edutainment dapat menjadikan peserta didik aktif dalam kegiatan belajar dan menyenangkan. Namun, memerlukan waktu yang cukup lama dalam pembelajarannya dan kurang efektif digunakan dalam kelas besar; dan (2) Pada pembelajaran fisika, model discovery learning berbasis edutainment digunakan untuk memahami materi yang cukup sulit dengan cara yang menyenangkan.Physics learning is learning about natural phenomena and trying to find the relationship between reality in nature. It is necessary to have models and methods to explain these natural phenomena in the physics learning process. One model that is expected to match physics learning characteristics is the discovery learning model based on edutainment. This study aims: (1) To describe the advantages and disadvantages of the discovery learning model based on edutainment models; (2) To describe the application of the discovery learning model based on edutainment models in physics learning. The data collection method in this research is a literature study and literature review. This research data use secondary data, then the data obtained is processed by content analysis. The results showed that: (1) Discovery learning model based on edutainment could make students active in learning activities because it is fun; however, the learning process requires a long time and is less effective when used in a class with a large number of students; and (2) In physics learning, the discovery learning model based on edutainment is used to understand difficult material in a fun way.
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J. Richardson, Alan. "The discovery of cumulative knowledge." Accounting, Auditing & Accountability Journal 31, no. 2 (February 19, 2018): 563–85. http://dx.doi.org/10.1108/aaaj-08-2014-1808.
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Purpose The purpose of this paper is to provide guidance for designing and generating cumulative knowledge based on qualitative research. Design/methodology/approach The paper draws on the philosophy of science and specific examples of qualitative studies in accounting that have claimed a cumulative contribution to knowledge to develop a taxonomy of theoretically justified approaches to generating cumulative knowledge from qualitative research. Findings The paper argues for a definition of cumulative knowledge that is inclusive of anti-realist research, i.e. knowledge is cumulative if it increases the extent and density of intertextual linkages in a field. It identifies the possibility of cumulative qualitative research based on extensions to the scope of the knowledge and the depth of the knowledge. Extensions to the scope of the knowledge may include expanding the time periods, context, and/or theoretical perspective used to explore a phenomenon. Extensions to the depth of the knowledge may include new empirical knowledge, methodological pluralism, theory elaboration, or analytic generalization. Individual studies can demonstrate their contribution to cumulative knowledge by locating their research within a typology/taxonomy that makes explicit the relationship of current research to past, and potential, research. Research limitations/implications The taxonomy may be useful to qualitative researchers designing and reporting research that will have impact on the literature. Social implications The increased use of research impact as an evaluation metric has the potential to handicap the development qualitative research which is often thought of as generating non-cumulative knowledge. The taxonomy and the strategies for establishing cumulative impact may provide a means for this approach to research to establish its importance as a contribution to knowledge. Originality/value The concept of cumulative knowledge has not been systematically applied to research based on qualitative methods.
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Zhang, Changlu, Haojie Fan, Jian Zhang, Qiong Yang, and Liqian Tang. "Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method." Entropy 25, no. 6 (June 13, 2023): 935. http://dx.doi.org/10.3390/e25060935.
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Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted.
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Yerlikaya, Seda, Ewurama D. A. Owusu, Augustina Frimpong, Robert Kirk DeLisle, and Xavier C. Ding. "A Dual, Systematic Approach to Malaria Diagnostic Biomarker Discovery." Clinical Infectious Diseases 74, no. 1 (October 28, 2021): 40–51. http://dx.doi.org/10.1093/cid/ciab251.
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Abstract Background The emergence and spread of Plasmodium falciparum parasites that lack HRP2/3 proteins and the resulting decreased utility of HRP2-based malaria rapid diagnostic tests (RDTs) prompted the World Health Organization and other global health stakeholders to prioritize the discovery of novel diagnostic biomarkers for malaria. Methods To address this pressing need, we adopted a dual, systematic approach by conducting a systematic review of the literature for publications on diagnostic biomarkers for uncomplicated malaria and a systematic in silico analysis of P. falciparum proteomics data for Plasmodium proteins with favorable diagnostic features. Results Our complementary analyses led us to 2 novel malaria diagnostic biomarkers compatible for use in an RDT format: glyceraldehyde 3-phosphate dehydrogenase and dihydrofolate reductase-thymidylate synthase. Conclusions Overall, our results pave the way for the development of next-generation malaria RDTs based on new antigens by identifying 2 lead candidates with favorable diagnostic features and partially de-risked product development prospects.
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Imran, Faiza Qayyum, Do-Hyeun Kim, Seon-Jong Bong, Su-Young Chi, and Yo-Han Choi. "A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery." Materials 15, no. 4 (February 15, 2022): 1428. http://dx.doi.org/10.3390/ma15041428.
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Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments’ challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.
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Liu, Chang, Kaimin Xiao, Cuinan Yu, Yipin Lei, Kangbo Lyu, Tingzhong Tian, Dan Zhao, Fengfeng Zhou, Haidong Tang, and Jianyang Zeng. "A probabilistic knowledge graph for target identification." PLOS Computational Biology 20, no. 4 (April 5, 2024): e1011945. http://dx.doi.org/10.1371/journal.pcbi.1011945.
Full textAbstract:
Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially the machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.
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Dunn, Heather, Laurie Quinn, Susan J. Corbridge, Kamal Eldeirawi, Mary Kapella, and Eileen G. Collins. "Cluster Analysis in Nursing Research: An Introduction, Historical Perspective, and Future Directions." Western Journal of Nursing Research 40, no. 11 (May 16, 2017): 1658–76. http://dx.doi.org/10.1177/0193945917707705.
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The use of cluster analysis in the nursing literature is limited to the creation of classifications of homogeneous groups and the discovery of new relationships. As such, it is important to provide clarity regarding its use and potential. The purpose of this article is to provide an introduction to distance-based, partitioning-based, and model-based cluster analysis methods commonly utilized in the nursing literature, provide a brief historical overview on the use of cluster analysis in nursing literature, and provide suggestions for future research. An electronic search included three bibliographic databases, PubMed, CINAHL and Web of Science. Key terms were cluster analysis and nursing. The use of cluster analysis in the nursing literature is increasing and expanding. The increased use of cluster analysis in the nursing literature is positioning this statistical method to result in insights that have the potential to change clinical practice.
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Toy, Seyma, and Yusuf Secgin. "Cadaver embalming and fixing solutions from past to present." Journal of Clinical Medicine of Kazakhstan 19, no. 5 (October 27, 2022): 9–11. http://dx.doi.org/10.23950/jcmk/12551.
Full textAbstract:
Cadaver and organ embalming-fixing solutions have a long history. The aim of this study is to follow this historical adventure step by step and to consider the point of embalming and fixation solutions. This study was carried out on the literature published to Google Scholar, PubMed, and Science Direct between 2000 and 2021. During the search, “cadaver, cadaver dissection, cadaver detection, cadaver use, cadaver embalming, cadaver preservation and organ preservation” were chosen as keywords. The discovery of formaldehyde in 1869 was clearly a turning point for cadaver and organ embalming-fixing solutions, and formaldehyde-based solutions are widely used even today. However, in addition to formaldehyde-based solutions, there are methods such as plastinization, paraffinization, resin embedding methods. It is clearly seen in the literature that formaldehyde-based solutions have serious side effects in terms of human health. Therefore, scientists have developed different methods. However, these methods have both application difficulties and accessibility problems compared to formaldehyde-based methods. Solutions that can prevent these problems should be produced in a short time.
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Pristiana, Chofifah Puji, Hasna Hanifah Safitri, and Setia Rahmawan. "Studi Literatur: Implementasi Model Pembelajaran Discovery Learning Terhadap Peningkatan Hasil Belajar Peserta Didik Materi Kimia SMA." Arfak Chem: Chemistry Education Journal 7, no. 1 (June 2, 2024): 570–80. http://dx.doi.org/10.30862/accej.v7i1.589.
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The aim of this research is to characterize the extent to which the discovery learning model can improve student learning outcomes in high school chemistry material and the impact of this model on classroom learning. This research employs a descriptive-qualitative literature study methodology, based on the publication of scientific journal articles. The findings of this research indicate that the discovery learning model, when applied in each cycle, will improve learning outcomes, thus indicating that this model may be a substitute for conventional chemistry teaching methods. However, this model also requires reflection, adjustment, and continuous improvement in the learning process