Relevant bibliographies by topics / Literature based discovery – methods

Academic literature on the topic 'Literature based discovery – methods'

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Published: 26 July 2024
Last updated: 27 July 2024

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Journal articles on the topic "Literature based discovery – methods"

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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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Dissertations / Theses on the topic "Literature based discovery – methods"

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Fleig, John David. "Citationally Enhanced Semantic Literature Based Discovery." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1082.

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We are living within the age of information. The ever increasing flow of data and publications poses a monumental bottleneck to scientific progress as despite the amazing abilities of the human mind, it is woefully inadequate in processing such a vast quantity of multidimensional information. The small bits of flotsam and jetsam that we leverage belies the amount of useful information beneath the surface. It is imperative that automated tools exist to better search, retrieve, and summarize this content. Combinations of document indexing and search engines can quickly find you a document whose content best matches your query - if the information is all contained within a single document. But it doesn’t draw connections, make hypotheses, or find knowledge hidden across multiple documents. Literature-based discovery is an approach that can uncover hidden interrelationships between topics by extracting information from existing published scientific literature. The proposed study utilizes a semantic-based approach that builds a graph of related concepts between two user specified sets of topics using semantic predications. In addition, the study includes properties of bibliographically related documents and statistical properties of concepts to further enhance the quality of the proposed intermediate terms. Our results show an improvement in precision-recall when incorporating citations.
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Radovanovic, Aleksandar. "Concept Based Knowledge Discovery from Biomedical Literature." Thesis, Online access, 2009. http://etd.uwc.ac.za/usrfiles/modules/etd/docs/etd_gen8Srv25Nme4_9861_1272229462.pdf.

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Aamot, Elias. "Literature-based knowledge discovery in climate science." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-27047.

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Climate change caused by anthropogenic activity is one of the biggest challenges of our time. Researchers are striving to understand the effects of global warming on the ecological systems of the oceans, and how these ecological systems influence the global climate, a line of research that is crucial in order to counteract or adapt to the effects of global warming. A major challenge that researchers in this area are facing, is the huge amount of potentially relevant literature, as insights from widely different fields such as biology, chemistry, climatology and oceanography can prove crucial in understanding the effects of global warming on the oceans. To alleviate some of the work load from researchers, information extraction tools can be used to extract relevant information from the scientific literature automatically, and discovery support tools can be developed to assist researchers in their efforts. This master thesis conducts fundamental research into the development of discovery support tools for oceanographic climate science, focusing primarily on the information extraction component.
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Yildiz, Meliha Yetisgen. "Using statistical and knowledge-based approaches for literature-based discovery /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7178.

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Cameron, Delroy Huborn. "A Context-Driven Subgraph Model for Literature-Based Discovery." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1417034001.

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Henry, Sam. "Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5855.

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The exponential growth of scientific literature is creating an increased need for systems to process and assimilate knowledge contained within text. Literature Based Discovery (LBD) is a well established field that seeks to synthesize new knowledge from existing literature, but it has remained primarily in the theoretical realm rather than in real-world application. This lack of real-world adoption is due in part to the difficulty of LBD, but also due to several solvable problems present in LBD today. Of these problems, the ones in most critical need of improvement are: (1) the over-generation of knowledge by LBD systems, (2) a lack of meaningful evaluation standards, and (3) the difficulty interpreting LBD output. We address each of these problems by: (1) developing indirect relatedness measures for ranking and filtering LBD hypotheses; (2) developing a representative evaluation dataset and applying meaningful evaluation methods to individual components of LBD; (3) developing an interactive visualization system that allows a user to explore LBD output in its entirety. In addressing these problems, we make several contributions, most importantly: (1) state of the art results for estimating direct semantic relatedness, (2) development of set association measures, (3) development of indirect association measures, (4) development of a standard LBD evaluation dataset, (5) division of LBD into discrete components with well defined evaluation methods, (6) development of automatic functional group discovery, and (7) integration of indirect relatedness measures and automatic functional group discovery into a comprehensive LBD visualization system. Our results inform future development of LBD systems, and contribute to creating more effective LBD systems.
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Li, Lizhi. "Graphic Network based Methods in Discovering TFBS Motifs." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1325276730.

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Seki, Kazuhiro. "Literature-based discovery finding implicit associations between genes and diseases /." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3232584.

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Thesis (Ph.D.)--Indiana University, School of Library and Information Science, 2006.
"Title from dissertation home page (viewed July 10, 2007)." Source: Dissertation Abstracts International, Volume: 67-08, Section: A, page: 2796. Adviser: Javed Mostafa.
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Yu, Zhiguo. "Cooperative Semantic Information Processing for Literature-Based Biomedical Knowledge Discovery." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/33.

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Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsupervised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the “bag-of-words” assumption of topic models to the “bag-of-important-phrases” assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the “bag-of-important-phrases” approach is better at identifying semantic themes than the standard “bag-of-words” approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard “word intrusion test” to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to evaluate how well the system helps biomedical researchers explore a set of documents to discover previously hidden semantic themes and connections. The methodology for this study has been successfully used to evaluate other knowledge-discovery tools in biomedicine.
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Alvarsson, Jonathan. "Ligand-based Methods for Data Management and Modelling." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-248964.

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Drug discovery is a complicated and expensive process in the billion dollar range. One way of making the drug development process more efficient is better information handling, modelling and visualisation. The majority of todays drugs are small molecules, which interact with drug targets to cause an effect. Since the 1980s large amounts of compounds have been systematically tested by robots in so called high-throughput screening. Ligand-based drug discovery is based on modelling drug molecules. In the field known as Quantitative Structure–Activity Relationship (QSAR) molecules are described by molecular descriptors which are used for building mathematical models. Based on these models molecular properties can be predicted and using the molecular descriptors molecules can be compared for, e.g., similarity. Bioclipse is a workbench for the life sciences which provides ligand-based tools through a point and click interface.  The aims of this thesis were to research, and develop new or improved ligand-based methods and open source software, and to work towards making these tools available for users through the Bioclipse workbench. To this end, a series of molecular signature studies was done and various Bioclipse plugins were developed. An introduction to the field is provided in the thesis summary which is followed by five research papers. Paper I describes the Bioclipse 2 software and the Bioclipse scripting language. In Paper II the laboratory information system Brunn for supporting work with dose-response studies on microtiter plates is described. In Paper III the creation of a molecular fingerprint based on the molecular signature descriptor is presented and the new fingerprints are evaluated for target prediction and found to perform on par with industrial standard commercial molecular fingerprints. In Paper IV the effect of different parameter choices when using the signature fingerprint together with support vector machines (SVM) using the radial basis function (RBF) kernel is explored and reasonable default values are found. In Paper V the performance of SVM based QSAR using large datasets with the molecular signature descriptor is studied, and a QSAR model based on 1.2 million substances is created and made available from the Bioclipse workbench.
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Books on the topic "Literature based discovery – methods"

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Klon, Anthony E., ed. Fragment-Based Methods in Drug Discovery. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2486-8.

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Bruza, Peter, and Marc Weeber, eds. Literature-based Discovery. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68690-3.

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Structure-based drug discovery. New York: Humana Press/Springer, 2012.

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Pilania, Ghanshyam, Prasanna V. Balachandran, James E. Gubernatis, and Turab Lookman. Data-Based Methods for Materials Design and Discovery. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-02383-5.

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Triantaphyllou, Evangelos. Data Mining and Knowledge Discovery via Logic-Based Methods. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1630-3.

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Anand, D. K. Simulation-based innovation and discovery: Energetics applications. College Park, Maryland: CALCE EPSC Press, 2011.

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Harren, Jhoti, and Leach Andrew R, eds. Structure-based drug discovery. Dordrecht: Springer, 2007.

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Edward, Zartler, and Shapiro Michael, eds. Fragment-based drug discovery: A practical approach. Hoboken, N.J: John Wiley & Sons, 2008.

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Triantaphyllou, Evangelos. Data mining and knowledge discovery via logic-based methods: Theory, algorithms, and applications. New York: Springer, 2010.

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Bienstock, Rachelle J. Library design, search methods, and applications of fragment-based drug design. Washington, DC: American Chemical Society, 2011.

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Book chapters on the topic "Literature based discovery – methods"

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Sehgal, A. K., X. Y. Qiu, and P. Srinivasan. "Analyzing LBD Methods using a General Framework." In Literature-based Discovery, 75–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68690-3_6.

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Korhonen, Anna, Yufan Guo, Simon Baker, Meliha Yetisgen-Yildiz, Ulla Stenius, Masashi Narita, and Pietro Liò. "Improving Literature-Based Discovery with Advanced Text Mining." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 89–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24462-4_8.

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Kibwami, Nathan, and Apollo Tutesigensi. "Using literature-based discovery in built environment research." In Secondary Research Methods in the Built Environment, 193–206. Abingdon, Oxon; New York, NY: Routledge/Taylor & Francis Group, 2021.: Routledge, 2021. http://dx.doi.org/10.1201/9781003000532-14.

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Eom, Jae-Hong, Sun Kim, Seong-Hwan Kim, and Byoung-Tak Zhang. "A Tree Kernel-Based Method for Protein-Protein Interaction Mining from Biomedical Literature." In Knowledge Discovery in Life Science Literature, 42–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11683568_4.

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Chen, Chaomei, and Min Song. "Literature-Based Discovery." In Representing Scientific Knowledge, 263–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62543-0_7.

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Kostoff, R. N. "Where is the Discovery in Literature-Based Discovery?" In Literature-based Discovery, 57–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68690-3_5.

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Swanson, D. R. "Literature-Based Discovery? The Very Idea." In Literature-based Discovery, 3–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68690-3_1.

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Hersh, W. "Information Retrieval in Literature-Based Discovery." In Literature-based Discovery, 153–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68690-3_10.

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Koike, A. "Biomedical Application of Knowledge Discovery." In Literature-based Discovery, 173–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68690-3_11.

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Smalheiser, Neil R., and Vetle I. Torvik. "The Place of Literature-Based Discovery in Contemporary Scientific Practice." In Literature-based Discovery, 13–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68690-3_2.

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Conference papers on the topic "Literature based discovery – methods"

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Gulec, Fatih Mehmet, Tahir Bicakci, Ebru Akcapinar Sezer, Hayri Sever, and Vijay V. Raghavan. "Analyzing the Effectiveness of Pruning and Grouping Methods Used in Literature-Based Discovery Tools." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.43.

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Sang, Shengtian, Zhihao Yang, Xiaoxia Liu, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang, Liang Yang, Kan Xu, and Yijia Zhang. "A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621423.

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Gericke, Kilian, Celeste Roschuni, and Julia Kramer. "Discovery and Evaluation of Design Methods in Practice: An Empirical Study." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47387.

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Web platforms and literature on design methods allow users to search for existing methods based on the method’s name and stage of use. Little support is provided to assess whether a method is appropriate for the task at hand and the context where the method will be applied. In this explorative study, patterns in searching, selecting, assessing and exchanging experiences with peers in professional practice were analyzed across a range of disciplines. This work reports on our findings from interviews conducted with practicing engineers and designers. Several similarities in the way practitioners find, select and assess new methods were found and interesting differences were identified for the practices in exchanging experiences with peers. In this work, we present the findings of our interview study along with directions for future work to better understand design processes in professional practice.
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Rastegar-Mojarad, Majid, Ravikumar Komandur Elayavilli, Dingcheng Li, Rashmi Prasad, and Hongfang Liu. "A new method for prioritizing drug repositioning candidates extracted by literature-based discovery." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359766.

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M, Saranya, Arockia Xavier Annie R, and Geetha T V. "Relation Extraction between Biomedical Entities from Literature using Semi- Supervised Learning Approach." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112306.

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Now-a-days, people around the world are infected by many new diseases. The cost of developing or discovering a new drug for the newly discovered disease is very high and prolonged process. These could be eliminated with the help of already existing resources. To identify the candidates from the existing drugs, we need to extract the relation between the drug, target and disease by textming a large-scale literature. Recently, computational approaches which is used for identifying the relationships between the entities in biomedical domain are appearing as an active area of research for drug discovery as it needs more man power. Due to the limited computational approaches, the relation extraction between drug-gene and genedisease association from the unstructured biomedical documents is very hard. In this work, we proposed a semi-supervised approach named pattern based bootstrapping method to extract the direct relations between drug, gene and disease from the biomedical literature. These direct relationships are used to infer indirect relationships between entities such as drug and disease. Now these indirect relationships are used to determine the new candidates for drug repositioning which in turn will reduce the time and the patient’s risk.
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Fonseca, Gabriel Barbosa, Zenilton K. G. Patrocínio Jr, Guillaume Gravier, and Silvio Jamil F. Guimarães. "Multimodal person discovery using label propagation over speaking faces graphs." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8312.

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The indexing of large datasets is a task of great importance, since it directly impacts on the quality of information that can be retrieved from these sets. Unfortunately, some datasets are growing in size so fast that manually indexing becomes unfeasible. Automatic indexing techniques can be applied to overcome this issue, and in this study, a unsupervised technique for multimodal person discovery is proposed, which consists in detecting persons that are appearing and speaking simultaneously on a video and associating names to them. To achieve that, the data is modeled as a graph of speaking-faces, and names are extracted via OCR and propagated through the graph based on audiovisual relations between speaking faces. To propagate labels, two graph based methods are proposed, one based on random walks and the other based on a hierarchical approach. In order to assess the proposed approach, we use two graph clustering baselines, and different modality fusion approaches. On the MediaEval MPD 2017 dataset, the proposed label propagation methods outperform all literature methods except one, which uses a different approach on the pre-processing step. Even though the Kappa coefficient indicates that the random walk and the hierarchical label propagation produce highly equivalent results, the hierarchical propagation is more than 6 times faster than the random walk under same configurations.
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Sood, T., and A. Gurmu. "Reusing and repurposing of glass waste: A literature review." In 10th World Construction Symposium. Building Economics and Management Research Unit (BEMRU), University of Moratuwa, 2022. http://dx.doi.org/10.31705/wcs.2022.56.

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An increase in demolition activities has led to the generation of large amounts of glass waste. Due to its non-biodegradable nature, glass can stay in landfills for longer periods without decomposition; thus, it occupies a large volume of landfills. This study aimed to analyze the potential reuses and repurposing of glass waste in the construction industry. A systematic review of the literature was conducted, and secondary data were extracted. The data were analyzed qualitatively to achieve the objective of the study. The generation of glass waste in four different parts of the world, namely: Hong Kong, Australia, Europe and the USA has been studied to get an estimate for the increase in glass waste trends. The generation of glass waste saw a downfall from 2006 to 2010, was then steady from 2011 to 2016 and then slightly rose. A high volume of glass waste in landfills and its non-biodegradable nature has made it essential for the discovery of new methods of reuse and recycling of glass waste. Some of the potential reuse and repurposing options include Aggregate for Concrete, Filtration Media, Glass Fibres, Blast Abrasive, Roof Coating, Ceramic Based Products, Burnt Bricks, Low-Temperature Stoneware Tiles, Insulation, and Decorative Materials. The paper provides useful information to various stakeholders in the construction industry to understand how and where glass waste can be reused.
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Guo, Tinghao, Jiarui Xu, Yue Sun, Yilin Dong, Neal E. Davis, and James T. Allison. "Network Analysis of Design Automation Literature." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67361.

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In this paper we present a study of citation and co-authorship networks for articles from the ASME Design Automation Conference (DAC) during the years 2002–2015. We identify key authors, show that the co-authorship network exhibits the small world network property, and reveal other insights from network structure. Results from two topic modeling methods are presented. A frequency-based model was developed to explore DAC topic distribution and evolution. Citation analysis was also conducted for each core topic. A correlation matrix and association rule mining were used to discover topic relations and to gain insights for research gaps and recommendations. A recently developed unsupervised learning algorithm, propagation mergence (PM), was applied to the DAC citation network. Influential papers and major clusters were identified and visualizations are presented. The resulting insights may be beneficial to the engineering design research community, especially with respect to determining future directions and possible actions for improvement. The data set used here is limited. Expanding to include additional relevant conference proceedings and journal articles in the future would offer a more complete understanding of the engineering design research literature.
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Bougoulia, Eirini, and Michail Glykas. "An Integrated Proposal for a Knowledge Management Implementation & Maturity Assessment Model." In Eighth International Scientific-Business Conference LIMEN Leadership, Innovation, Management and Economics: Integrated Politics of Research. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2022. http://dx.doi.org/10.31410/limen.2022.179.

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The goal of the paper is to suggest a comprehensive and inte­grated knowledge management implementation and maturity assessment model based on the most common critical success factors and corporate en­ablers discovered during a systematic overview of the knowledge manage­ment maturity models and the related literature review. Summarizing the re­view leads to the discovery of KMMMs CSFs and key themes, while simultane­ously examining the idea of standardization through accepted KM standards and their core principles. To provide a model that can be used by both practi­tioners and researchers in the future to improve organizational performance and to be used as a tool for knowledge management performance measure­ment, the implementation stages of the proposed framework, the maturity levels, the proposed assessment measuring tools and methods are presented in an approach that encompasses the core guidelines of ISO 30401.
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Stanciu, Mihaela. "DIGITAL METHODS VS EXPERIENTIAL LEARNING METHODS. DIDACTIC APPLICATIONS." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-079.

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The study below is a component of didactic research that aims to adapt the teaching methods and tools used in the Romanian Language and Literature lesson to software applications in the education field. As it was an optional subject for the high-school students, during a semester I studied, the transition from experiential learning methods inspired by the art of theatre to the digital ones such as the clicker response system, using the Mentimeter app. The purpose of the research was to observe the students' level of involvement in the lesson. I was able to monitor it clearly by creating different constructivist learning situations. The subjects of this study were 10th-grade students from two distinct classes, a sample of 55 students. The mixed research methodology used in this study highlights both quantitative and qualitative methods, pursuing a balance in data collection, analysis and interpretation. The first research method was the survey, two questionnaires were analyzed, the first one aimed the students' opinion on digital methods, and the second one, the experiential methods. Another method of applied research was qualitative observation which materialized in the analysis of students' activity results. In order to interpret the collected data from the two methods there were considered the analysis of the time distribution, the level of students' participation and the type of learning tasks. After being statistically processed, the data collected led to preliminary results of the research showing a substantial involvement of the students in accomplishing tasks. Regarding the interdisciplinary strategies, where were used the dramatic methods and techniques, the students reacted well from the point of view of the new knowledge acquisition, but several hours were needed to reach the objectives. On the other hand, students reacted positively to the digital clicker response system, at the same time, an important amount of time was saved in the lesson. The comparative observation of the two examples of collaborative work showed that through the use of methodological interdisciplinarity, in particular the interaction through the dramatic game and the response system Mentimeter, based on the use of the smartphone, students can make connections with life situations and their own experience, which translates to active participation and involvement. Moreover, by using the Mentimeter app we discovered that the use of smartphones during the lesson can become an opportunity for students to cultivate a positive attitude towards school and the values and beliefs taught in the Romanian Language and Literature lesson. Dramatic games and technology remain two areas of maximum interest for students and contribute to the optimization of the teaching methods.
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Reports on the topic "Literature based discovery – methods"

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Bruce, Bill, Melissa Gould, Tom Bubenik, Chris Alexander, and Michael Rosenfeld. PR-186-204504-R01 PRCI Pipeline Repair Manual 2021 Edition. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2022. http://dx.doi.org/10.55274/r0012221.

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This 2021 Edition of the PRCI Pipeline Repair Manual is an updated version of the original manual that was published 1994 and updated in 2006. The 2021 Edition discusses response to anomaly or defect discovery, reviews repair methods, identifies appropriate repairs for various types of defects, and provides generic guidelines for use of various repair methods. DNV has reviewed existing and emerging pipeline repair technologies and evaluated them in comparison with those in the previous versions of the manual. The review was based on published literature, vendor literature, and industry experience. The 2021 Edition of the Pipeline Repair Manual contains added and updated information on repair technologies gleaned from the literature, vendor publications, and industry experience. Without losing sight of well-established repair methods that have served the industry well, the most recent update focused on repair methods and practices that are commonplace or have emerged and have become commonplace since the previous version of the manual was published. This manual does not identify or prescribe all repair methods for all defects or anomalies. Instead, the manual concentrates on commonly encountered defects and commonly used repair methodologies for those defects. Other repair techniques may be equally valid depending on the specific conditions encountered. This 2021 Edition of the manual does not provide specific guidance on current regulations or on code interpretation. National and international codes and regulations change rapidly, and code interpretation is best left to individual companies. Footnotes identify specific pertinent sections of selected US regulations. Click here for related webinar.
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Blei, David M. Dynamic and Supervised Topic Models for Literature-Based Discovery. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada554210.

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Ren, Hongguo, Minqi Shi, and Jing Zhang. Research contents, methods and prospects of Emotional Architecture based on systematic literature review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2024. http://dx.doi.org/10.37766/inplasy2024.4.0004.

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Sakleshpur, Venkata A., Monica Prezzi, Rodrigo Salgado, and Mir Zaheer. CPT-Based Geotechnical Design Manual, Volume 2: CPT-Based Design of Foundations—Methods. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317347.

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This manual provides guidance on how to use the cone penetration test (CPT) for site investigation and foundation design. The manual has been organized into three volumes. Volume 1 covers the execution of CPT-based site investigations and presents a comprehensive literature review of CPT-based soil behavior type (SBT) charts and estimation of soil variables from CPT results. Volume 2 covers the methods and equations needed for CPT data interpretation and foundation design in different soil types, while Volume 3 includes several example problems (based on instrumented case histories) with detailed, step-by-step calculations to demonstrate the application of the design methods. The methods included in the manual are current, reliable, and demonstrably the best available for Indiana geology based on extensive CPT research carried out during the past two decades. The design of shallow and pile foundations in the manual is based on the load and resistance factor design (LRFD) framework. The manual also indicates areas of low reliability and limited knowledge, which can be used as indicators for future research.
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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.

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The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.
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Rawlings, Deshanie, Gabriel Moore, Anton du Toit, Hyun Song, Eileen Goldberg, Moin Uddin Ahmed, and Hir S Jani. The effectiveness of opioid treatment delivery methods. The Sax Institute, May 2022. http://dx.doi.org/10.57022/zwhw5494.

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This Evidence Snapshot review identifies innovative models of service delivery for Opioid Treatment Programs and their costs. The review was conducted to support improving access to treatment in NSW. Innovative models of opioid treatment identified were digital health, primary care, low threshold, group-based treatment, and mobile outreach as well as a combination of these models. Telemedicine models were most frequently mentioned in the peer reviewed literature as these addressed many traditional barriers to treatment, particularly for rural and hard to reach communities. Primary care models were most frequently mentioned in the grey literature as using innovative approaches which included the use of designated nonphysician staff to perform the care coordination role and tiered care models with centralised intake and stabilisation of patients with ongoing management in community settings. None of the included papers mentioned costs.
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Alwagdani, Abdullah. Review Of mPGES-1 Inhibitors Based On The Benzoxazole And Its Isostere Scaffold For The Treatment Of Inflammatory Diseases. University of Tennessee Health Science Center, June 2024. http://dx.doi.org/10.21007/com.lsp.2024.0021.

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The vital role of the prostanoid pathway in inflammation, pain, cancer, Alzheimer’s and many other diseases has attracted the drug discovery community to discover targets for therapeutic development. Although existing non-steroidal anti-inflammatory drugs (NSAIDs) inhibiting cyclooxygenases (COX) are widely used, the side effects of these NSAIDs limit the ling time medication. Microsomal prostaglandin E synthase-1 (mPGES-1) is an attractive target that is overexpressed during inflammations, and it could be a safe alternative to NSAIDs for treating inflammatory diseases.Since the discovery of mPGES-1 in 1997, many inhibitors have been developed since 2001. Only a few compounds were able to make it to clinical trials, and only two molecules are in phase II clinical trials. Among the mPGES-1 inhibitors, benzoxazole, indole, and benzimidazole are the most explored chemical scaffolds, especially benzimidazole. One of the two inhibitors in the clinical trials is based on this scaffold. Here, we provide a review of mPGES-1's role in inflammation and inhibitors based on these scaffolds that are reported in the literature.
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Sakleshpur, Venkata A., Monica Prezzi, Rodrigo Salgado, and Mir Zaheer. CPT-Based Geotechnical Design Manual, Volume 3: CPT-Based Design of Foundations—Example Problems. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317348.

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This manual provides guidance on how to use the cone penetration test (CPT) for site investigation and foundation design. The manual has been organized into three volumes. Volume 1 covers the execution of CPT-based site investigations and presents a comprehensive literature review of CPT-based soil behavior type (SBT) charts and estimation of soil variables from CPT results. Volume 2 covers the methods and equations needed for CPT data interpretation and foundation design in different soil types, while Volume 3 includes several example problems (based on instrumented case histories) with detailed, step-by-step calculations to demonstrate the application of the design methods. The methods included in the manual are current, reliable, and demonstrably the best available for Indiana geology based on extensive CPT research carried out during the past two decades. The design of shallow and pile foundations in the manual is based on the load and resistance factor design (LRFD) framework. The manual also indicates areas of low reliability and limited knowledge, which can be used as indicators for future research.
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Chiavaroli, Neville, Kate Reid, Pru Mitchell, and Jenny Trevitt. Systematic review of the literature on professional education accreditation. Australian Council for Educational Research, 2020. http://dx.doi.org/10.37517/978-1-74286-711-3.

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This report is a critical and systematic review of the literature on professional education accreditation models, associated methods, and the effectiveness and impact of each model. The review was conducted to inform a review and update of the Royal College of Veterinary Surgeons (RCVS) standards of accreditation for veterinary degrees. The purpose of the study was to rapidly appraise the evidence for the effectiveness, impact, and feasibility of different accreditation approaches, in order to inform best practices for the accreditation of professional education programs. The study focused on accreditation for programs which produce practice-ready graduates, including for veterinary programs. The authors searched several databases for articles published from 2000 to 2020, using search terms identified during a scoping phase, and applied a rapid review methodology in line with contextual, time, and resource requirements. The full-text review included 32 articles, from which a clear transition in the literature was seen from input- and process-based models (pre- and early 2000s) to outcomes-based models (in the 2000s and early 2010s). Continuous quality improvement and targeted models (including risk-based and thematic) represent more recent approaches in accreditation practice. However the review identified limited empirical evidence for the relative effectiveness of different accreditation approaches in professional education, although evidence for the more recent accreditation approaches is emerging.
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Oliveira, Lucas Gabriel Martins de. Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data. Inter-American Development Bank, July 2023. http://dx.doi.org/10.18235/0005004.

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The objective of this paper is to develop a basic framework for the implementation of a GDP nowcasting strategy using Brazilian data. Our goal is to identify a scalable strategy that allows us to project the Brazilian GDP in real time at any point during the current quarter. In the paper we detail the survey of classical techniques and also of techniques usually known by market practitioners as "machine learning methods". We survey the literature since the first work on estimating business cycles and document the evolution of this literature until the insertion of machine learning methods. Additionally, we perform backtesting exercises, estimate several candidate models for GDP nowcasting. Finally, we evaluate the forecasting power of all models against a naive model and a market expectations model. We demonstrate that a combination of machine learning models based on the distance of forecasts to the average market expectations defeats the fully informed market expectations, while the same is not possible for selected classical nowcasting models.
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