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Journal articles on the topic 'Knowledge graph profiling'
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Munir, Siraj, Syed Imran Jami, and Shaukat Wasi. "Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs." Open Computer Science 11, no. 1 (January 1, 2021): 294–304. http://dx.doi.org/10.1515/comp-2020-0209.
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Abstract In this work we have proposed a model for Citizen Profiling. It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries. Previously, most of the work lacks representation of Citizen Profile and have used surveillance for data acquisition. Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge Graphs. Our proposed solution is storage efficient as we have only stored data logs for Citizen Profiling instead of storing images, audio, and video for profiling purposes. Our proposed system can be extended to Smart City, Smart Traffic Management, Workplace profiling etc. Agent based mechanism can be used for data acquisition where each Citizen has its own agent. Another improvement can be to incorporate a decentralized version of database for maintaining Citizen profile.
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Gao, Hao, Yongqing Wang, Jiangli Shao, Huawei Shen, and Xueqi Cheng. "User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function." Entropy 24, no. 11 (November 4, 2022): 1603. http://dx.doi.org/10.3390/e24111603.
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Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as recommendation and user profiling. Recently, researchers proposed measuring the relevance of user-generated content to predict identity linkages of users. However, there are two challenging problems with existing content-based methods: first, barely considering the word similarities of texts is insufficient where the semantical correlations of named entities in the texts are ignored; second, most methods use time discretization technology, where the texts are divided into different time slices, resulting in failure of relevance modeling. To address these issues, we propose a user identity linkage model with the enhancement of a knowledge graph and continuous time decay functions that are designed for mitigating the influence of time discretization. Apart from modeling the correlations of the words, we extract the named entities in the texts and link them into the knowledge graph to capture the correlations of named entities. The semantics of texts are enhanced through the external knowledge of the named entities in the knowledge graph, and the similarity discrimination of the texts is also improved. Furthermore, we propose continuous time decay functions to capture the closeness of the posting time of texts instead of time discretization to avoid the matching error of texts. We conduct experiments on two real public datasets, and the experimental results show that the proposed method outperforms state-of-the-art methods.
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Du, Hongyan, Dejun Jiang, Junbo Gao, Xujun Zhang, Lingxiao Jiang, Yundian Zeng, Zhenxing Wu, et al. "Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network." Research 2022 (July 22, 2022): 1–15. http://dx.doi.org/10.34133/2022/9873564.
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Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.
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Yuan, Zixuan, Hao Liu, Renjun Hu, Denghui Zhang, and Hui Xiong. "Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4644–52. http://dx.doi.org/10.1609/aaai.v35i5.16594.
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Event-based corporate profiling aims to assess the evolving operational status of the corresponding corporate from its event sequence. Existing studies on corporate profiling have partially addressed the problem via (i) case-by-case empirical analysis by leveraging traditional financial methods, or (ii) the automatic profile inference by reformulating the problem into a supervised learning task. However, both approaches heavily rely on domain knowledge and are labor-intensive. More importantly, the task-specific nature of both approaches prevents the obtained corporate profiles from being applied to diversified downstream applications. To this end, in this paper, we propose a Self-Supervised Prototype Representation Learning (SePaL) framework for dynamic corporate profiling. By exploiting the topological information of an event graph and exploring self-supervised learning techniques, SePaL can obtain unified corporate representations that are robust to event noises and can be easily fine-tuned to benefit various down-stream applications with only a few annotated data. Specifically, we first infer the initial cluster distribution of noise-resistant event prototypes based on latent representations of events. Then, we construct four permutation-invariant self-supervision signals to guide the representation learning of the event prototype. In terms of applications, we exploit the learned time-evolving corporate representations for both stock price spike prediction and corporate default risk evaluation. Experimental results on two real-world corporate event datasets demonstrate the effectiveness of SePaL for these two applications.
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Li, Zhuliu, Tianci Song, Jeongsik Yong, and Rui Kuang. "Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion." PLOS Computational Biology 17, no. 4 (April 7, 2021): e1008218. http://dx.doi.org/10.1371/journal.pcbi.1008218.
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High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x,y) spatial coordinates (x-mode andy-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.
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Zhang, Xiang, Qingqing Yang, Jinru Ding, and Ziyue Wang. "Entity Profiling in Knowledge Graphs." IEEE Access 8 (2020): 27257–66. http://dx.doi.org/10.1109/access.2020.2971567.
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Amir, Muhammad Bilal, Yan Shi, Hehe Cao, Muhammad Yasir Ali, Muhammad Afaq Ahmed, Guy Smagghe, and Tong-Xian Liu. "Short Neuropeptide F and Its Receptor Regulate Feeding Behavior in Pea Aphid (Acyrthosiphon pisum)." Insects 13, no. 3 (March 13, 2022): 282. http://dx.doi.org/10.3390/insects13030282.
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Insect short neuropeptide F (sNPF), an ortholog of prolactin-releasing peptide of invertebrates, regulates diverse biological processes, including feeding, olfaction, locomotion, and sleep homeostasis in insects. However, its function is still unclear in an important model insect and agricultural pest, the pea aphid (Acyrthosiphon pisum). Here, we investigated short neuropeptide F (ApsNPF) and its receptor (ApsNPFR) in A. pisum. The sNPF gene contains three exons and two long introns. In addition, the genome contains a single sNPF receptor with seven transmembrane domains. Stage- and tissue-specific transcript profiling by qRT-PCR revealed that ApsNPF and ApsNPFR were mainly expressed in the central nervous system. The receptor was also detected in antennae, midgut, and integument. The highest expression levels were found in first instar nymphs compared to other developmental stages. Besides, the starvation-induced pattern indicated that the sNPF network depends on the nutritional state of the insect. An electrical penetration graph showed that probing time and phloem duration of A. pisum on broad bean plants decreased in response to dssNPF and dssNPFR in RNAi assays. sNPF silencing reduced the number of nymphs per female but not aphid survival. We believe that our results advance in-depth knowledge of the sNPF/sNPFR signaling cascade and its place in regulating feeding behavior in insects. In turn, it may contribute to the potential design of new strategies to control aphids, with a focus on the sNPF system.
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Jeong, Mira, Sangbae Kim, Yumei Li, Rui Chen, Premal Lulla, and Margaret Goodell. "Single Cell Profiling of DNMT3A-Mutant Progenitors Reveals LY86 As a Novel Pre-Leukemia Marker and Potential Therapeutic Target." Blood 134, Supplement_1 (November 13, 2019): 2724. http://dx.doi.org/10.1182/blood-2019-123597.
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Acute Myeloid Leukemia (AML) is a clonal disease of the hematopoietic system that initiated and sustained by self-renewing hematopoietic stem and progenitor cells (HSPC). Mutations in the de novo DNA methyltransferase 3A (DNMT3A) gene occur in approximately 25% of adult acute myeloid leukemias (AML). Although the mechanisms through which such mutations promote leukemogenesis remain unclear, we have previously shown that loss of the DNMT3A can inhibit normal hematopoietic differentiation (Challen, Nature Genetics, 2011), accounting for the emergence of DNMT3A-HSC clones as a predisposition to hematological malignancies (Yang, Cancer Cell, 2015). Therapies that selectively eliminate the initiating pre-leukemic population would greatly improve outcomes for affected patients. However, the identification as well as selective elimination of such a distinct population has been problematic because of the considerable overlap in gene expression profiles with bulk normal hematopoietic stem cells. Molecular targets during leukemia development have not been well elucidated due to lack of the real definitive markers, which is a significant knowledge gap and barrier for understanding clonal leukemogenesis and therapeutic applications. Single-cell RNA sequencing has emerged as a powerful tool to analyze new cell types, cellular heterogeneity and cell differentiation routes. This technique made important contributions to our understanding of hematopoietic stem and cancer cell heterogeneity and selective resistance of cancer cell subpopulations to molecularly targeted cancer therapies. To identify early events involved in pre-leukemic transformation, we have performed single-cell RNA-sequencing (scRNA-seq) in WT and Dnmt3a KO mice. Flow cytometry sorted wild-type and pre-leukemic Dnmt3a KO HSPC cells were captured using 10X genomics chromium platform. After genome mapping, dimensional reduction, and clustering using Cell ranger pipeline, we generated transcriptome data and integrated the data sets using Seurat. Approximately 8,000 cells from each group were sequenced, and each cell expressed 1800-4500 genes. Graph-based clustering analysis revealed 16 unique cell clusters in both WT and DNMT3A KO mice. Interestingly, when compared with WT mice, we observed a 10-fold expansion of a single cell cluster in Dnmt3a KO cells before the advent of overt leukemia. This cluster co-expresses several stem cell genes including well-known leukemic stem cell surface markers such as CD47, as well as several novel genes. Some of these novel genes, encode cell surface proteins such as Ly6c2 and Ly86. We further validated protein expressions in AML cell lines and primary AML blast. In conclusion, the discovery of novel cluster in DNMT3A KO mice, and the relative abundance of this cluster in pre-leukemic stage of DNMT3A KO mice indicates that they promote leukemogenesis and offers an opportunity to specifically target DNMT3A mutant pre-leukemic cells using T cell immunotherapy. Disclosures No relevant conflicts of interest to declare.
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Tang, Xulong, Mahmut Taylan Kandemir, and Mustafa Karakoy. "Mix and Match: Reorganizing Tasks for Enhancing Data Locality." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 2 (June 2021): 1–24. http://dx.doi.org/10.1145/3460087.
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Application programs that exhibit strong locality of reference lead to minimized cache misses and better performance in different architectures. However, to maximize the performance of multithreaded applications running on emerging manycore systems, data movement in on-chip network should also be minimized. Unfortunately, the way many multithreaded programs are written does not lend itself well to minimal data movement. Motivated by this observation, in this paper, we target task-based programs (which cover a large set of available multithreaded programs), and propose a novel compiler-based approach that consists of four complementary steps. First, we partition the original tasks in the target application into sub-tasks and build a data reuse graph at a sub-task granularity. Second, based on the intensity of temporal and spatial data reuses among sub-tasks, we generate new tasks where each such (new) task includes a set of sub-tasks that exhibit high data reuse among them. Third, we assign the newly-generated tasks to cores in an architecture-aware fashion with the knowledge of data location. Finally, we re-schedule the execution order of sub-tasks within new tasks such that sub-tasks that belong to different tasks but share data among them are executed in close proximity in time. The detailed experiments show that, when targeting a state of the art manycore system, our proposed compiler-based approach improves the performance of 10 multithreaded programs by 23.4% on average, and it also outperforms two state-of-the-art data access optimizations for all the benchmarks tested. Our results also show that the proposed approach i) improves the performance of multiprogrammed workloads, and ii) generates results that are close to maximum savings that could be achieved with perfect profiling information. Overall, our experimental results emphasize the importance of dividing an original set of tasks of an application into sub-tasks and constructing new tasks from the resulting sub-tasks in a data movement- and locality-aware fashion.
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Wang, Xiaxia, Tengteng Lin, Weiqing Luo, Gong Cheng, and Yuzhong Qu. "CKGSE: A Prototype Search Engine for Chinese Knowledge Graphs." Data Intelligence 4, no. 1 (2022): 41–65. http://dx.doi.org/10.1162/dint_a_00118.
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Abstract Nowadays, with increasing open knowledge graphs (KGs) being published on the Web, users depend on open data portals and search engines to find KGs. However, existing systems provide search services and present results with only metadata while ignoring the contents of KGs, i.e., triples. It brings difficulty for users' comprehension and relevance judgement. To overcome the limitation of metadata, in this paper we propose a content-based search engine for open KGs named CKGSE. Our system provides keyword search, KG snippet generation, KG profiling and browsing, all based on KGs' detailed, informative contents rather than their brief, limited metadata. To evaluate its usability, we implement a prototype with Chinese KGs crawled from OpenKG.CN and report some preliminary results and findings.
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Jain, Swachi, Ritesh Sachdev, Pranav Dorwal, Simmi Mehra, Smeeta Gajendra, Dharmendra Jain, Shalini Goel, Nitin Sood, and Vimarsh Raina. "Cosmic Mutational Analysis in Suspected Myeloproliferative Neoplasms Using Next Generation Sequencing with a Fifty Gene Panel." Blood 126, no. 23 (December 3, 2015): 5209. http://dx.doi.org/10.1182/blood.v126.23.5209.5209.
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Abstract Introduction: As per the 2008 World Health Organization (WHO) classification, Myeloproliferative neoplasms (MPN) are subclassified into eight clinicopathological groups.1 The discovery of activating JAK2 mutations revolutionized the approach to diagnosis of MPN. Recently there have been studies suggesting an increasing number of mutations distinct from JAK2 associated with MPN. The new mutations being studied are MPL with a mutation frequency of 1-5% commonly seen in Essential Thrombocytosis (ET) and Primary Myelofibrosis (PMF).2 IDH1 has a mutational frequency of 21% for blast phase of MPN and 4% for PMF.3 Other mutations that have been found to be coexistent with MPNs are EZH2, TP53 and TET2.4 Methods: A total of 79 cases with clinical suspicion of MPN were studied over one year. Based on WHO criteria, a total of 20 cases were diagnosed as MPN taking into account complete blood counts, bone marrow and cytogenetic studies with molecular profiling using next generation sequencing (NGS). Out of these, 13 cases were diagnosed as JAK2 positive and 7 as JAK2 negative MPN. All cases were studied for a panel of 50 mutations (Table 1) using NGS (Ion Torrent PGM) and a minimum coverage of 100x was considered to be significant. Table 1. Mutation Panel ABL1 EGFR GNAQ KRAS PTPN11 AKT1 ERBB2 GNAS MET RB11 ALK ERBB4 HNF1A MLH1 RET APC EZH2 HRAS MPL SMAD4 ATM FBXW7 IDH1 NOTCH1 SMARCB1 BRAF FGFR1 IDH2 NPM1 SMO CDH1 FGFR2 JAK2 NRAS SRC CDKN2A FGFR3 JAK3 PDGFRA STK11 CSF1R FLT3 KDR PIK3CA TP53 CTNNB1 GNA11 KIT PTEN VHL Results: Out of 20 MPN cases there were 3 cases of Polycythemia Vera (PV) 8 cases of ET and 9 cases of PMF. From the 14 mutations found in MPN, JAK 2 (65%) was the commonest followed by HRAS (45%), PDGFRA (45%), SMARCB1 (45%), Kit (40%), MET (25%), TP53 (20%), PIK3CA(20%), IDH1 (15%), STK11 (10%), APC (5%), FLT3(5%),PTEN (5%) and PTPN11(5%) (Graph1). Apart from JAK2, statistically significant mutations in the MPN group as compared to the non MPN group were Kit, TP53 and STK11 (Graph 2). Kit showed Single Nucleotide Polymorphism (SNP) with substitution of Adenosine (A) with Cytosine(C) on chr4:55593464 (hg19) and was statistically significant in MPN group as compared to non MPN group (40 % vs16.9 %, p value = 0.016). TP53 mutation showed a C to A SNP on chr17:7577036 (hg19) which was found more often in MPN groups than non MPN (20 % vs. 1.6 %, p value = 0.0018). STK11 mutation showed a C to Guanine (G) SNP on chr19:1223125 and a G insertion at chr19:1221320 and was found to be more associated with MPN than non MPN (10% vs. 1.6%, p value = 0.046) . However all other mutations were statistically insignificant. Graph 1: Total Mutations in MPN versus Non MPN Graph 2: Significant mutations in MPN *p-value: 0.016, **p-value: 0.046, ***p-value: 0.0018 In the MPN group, 13 cases were JAK2 positive and 7 cases were JAK2 negative. There was no statistical significance of presence of mutations between the two groups. In our study there was no significant association of IDH1 in MPN group in comparison to non MPN group ( 15% vs. 11.86%, p value = 0.35), in contrast to earlier studies.3 Conclusion: The mutations in Kit, TP53 and STK11 were found to be significantly more in cases of MPN as compared to non MPN. In future studies, different mutations present in MPN should be identified using NGS which will be crucial to not only diagnose and further characterize MPN cases but also for better understanding of the stepwise pathogenesis leading to cancer development in humans and to develop new targeted therapies. This is the first study of its kind in Indian population, to the best of our knowledge. References: 1. Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, et al., editors. WHO Classification of Tumours of Haematopoetic and Lymphoid Tissues. 4th ed. Lyon: International Agency for Research on Cancer (IARC); 2008. 2. Akpinar TS, Hancer VS, Nalcaci M, Diz-Kucukkaya R. MPL W515L/K Mutations in Chronic Myeloproliferative Neoplasms.Turk J Haematol. 2013 March; 30(1): 8-12. 3. Tefferi A, Lasho TL, Abdel-Wahab O, Guglielmelli P, Patel J, Caramazza D, et al. IDH1 and IDH2 mutation studies in 1473 patients with chronic-, fibrotic- or blast-phase essential thrombocythemia, polycythemia vera or myelofibrosis. Leukemia . 2010. July ;24(7):1302-9. 4. Lundberg P, Karow A, Nienhold R, Looser R, Hao-Shen H, Nissen I,et al., Clonal evolution and clinical correlates of somatic mutations in myeloproliferative neoplasms. Blood. 2014 Apr 3;123(14):2220-8. Figure 1. Figure 1. Figure 2. Figure 2. Disclosures No relevant conflicts of interest to declare.
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Toader, Bogdan, Assaad Moawad, Thomas Hartmann, and Francesco Viti. "A Data-Driven Scalable Method for Profiling and Dynamic Analysis of Shared Mobility Solutions." Journal of Advanced Transportation 2021 (January 18, 2021): 1–15. http://dx.doi.org/10.1155/2021/5943567.
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The advent of Internet of Things will revolutionise the sharing mobility by enabling high connectivity between passengers and means of transport. This generates enormous quantity of data which can reveal valuable knowledge and help understand complex travel behaviour. At the same time, it challenges analytics platforms to discover knowledge from data in motion (i.e., the analytics occur in real time as the event happens), extract travel habits, and provide reliable and faster sharing mobility services in dynamic contexts. In this paper, a scalable method for dynamic profiling is introduced, which allows the extraction of users’ travel behaviour and valuable knowledge about visited locations, using only geolocation data collected from mobile devices. The methodology makes use of a compact representation of time-evolving graphs that can be used to analyse complex data in motion. In particular, we demonstrate that using a combination of state-of-the-art technologies from data science domain coupled with methodologies from the transportation domain, it is possible to implement, with the minimum of resources, the next generation of autonomous sharing mobility services (i.e., long-term and on-demand parking sharing and combinations of car sharing and ride sharing) and extract from raw data, without any user input and in near real time, valuable knowledge (i.e., location labelling and activity classification).
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Matarese, Fabiola, Giancarlo Scalabrelli, and Claudio D'Onofrio. "Analysis of the expression of terpene synthase genes in relation to aroma content in two aromatic Vitis vinifera varieties." Functional Plant Biology 40, no. 6 (2013): 552. http://dx.doi.org/10.1071/fp12326.
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Grape (Vitis vinifera L.) flavour management in the vineyard requires knowledge of the derivation of individual flavour and aroma characteristics. Some of the most prevalent wine grape aroma constituents are terpenoids and this study represents a wide report about grape terpene synthase (TPS) gene transcript profiling in different tissues of two aromatic grapevine varieties, particularly flowers and developing berries, correlated with the accumulation patterns of free aroma compounds. All investigated genes belonging to the TPS-a and TPS-b subfamilies reached the highest expression in accordance with the peak of accumulation of the respective compounds. In the TPS-g subfamily, only one of the genes characterised for linalool synthases showed major transcript abundance in ripening berries, whereas the only geraniol synthase had a peak of expression in green berries and at the beginning of ripening, when geraniol concentration started to increase and overcome the linalool concentration. The genes identified in this study as being mainly responsible for linalool and geraniol synthesis during berry development, and the phenological phases in which they are mostly expressed, should be of interest to viticulturists and wine makers to improve decision making along the chain of production.
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Nebish, Anna, Javier Tello, Yolanda Ferradás, Rouben Aroutiounian, José Miguel Martínez-Zapater, and Javier Ibáñez. "SSR and SNP genetic profiling of Armenian grape cultivars gives insights into their identity and pedigree relationships." OENO One 55, no. 4 (November 10, 2021): 101–14. http://dx.doi.org/10.20870/oeno-one.2021.55.4.4815.
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The South Caucasus is recognised as the primary Vitis vinifera L. (grapevine) domestication centre and has a high diversity of wild and cultivated grapevines. Archaeological findings indicate that winemaking activities have existed in Armenia for more than 6,000 years, viticulture being one of the most important activities of the modern Armenian agricultural sector. Despite this relevance, some grapevines in local collections have not yet been properly identified, thus hindering the efficient conservation, characterisation and eventual use of autochthonous genetic resources. In the present study, a combined SNP and SSR profiling strategy was used for the genetic identification of a series of grapevine accessions from the Grape Collection of the International Academy of Viticulture and Winemaking in Nalbandyan, presumed to be autochthonous Armenian varieties. The results provided useful information for the correct identification of these genetic resources, revealing multiple cases of synonyms, homonyms and misnames. The genetic data made it possible to confirm the pedigree proposed for some of the cultivars identified in this study and to clarify the origin of others. In addition, we propose, for the first time, a series of new trios and duos involving autochthonous Armenian grapevines. The singularity of this genetic pool compared to other Western and Central European varieties, as well as the potential novel sources of variability in traits of interest (e.g., seedlessness) that were found, highlight the importance of improving knowledge of the Armenian grapevine genetic pool.
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Fuentes-Fayos, A. C., M. L. Gandía-González, A. Cano-Rojas, C. J. Blanco, E. M. Negro-Moral, Á. Toledano, M. J. Ramos, et al. "P13.11 Metabolomics and molecular profiling in glioma patients: an interactomic approach." Neuro-Oncology 21, Supplement_3 (August 2019): iii64—iii65. http://dx.doi.org/10.1093/neuonc/noz126.232.
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Abstract BACKGROUND A wider view of the interaction between different omic-domains is needed to identify potential biomarkers of low- and high-grade gliomas. Using an interactomic approach, we analyzed the correlation between radiological data, IDH mutation, gene expression profiling and metabolic signature in glioma samples. MATERIAL AND METHODS Tumor biopsies from 25 patients with clinical diagnosis of glioma were surgically collected during 2017–2019 at the senior author’s institution. Metabolomic data was obtained by high resolution 31P and 1H magnetic resonance spectroscopy (MRS, 19 metabolites quantified with LCModel). Gene expression profiling was performed using real-time qPCR of 19 genes related to energy metabolism. IDH1/2 common mutation (IDH1R132H/IDH2R172H) was verified by immunohistochemistry and amplicon sanger sequencing. All data was integrated using the R package mixOmics, and we built correlation network plot graphs and correlation maps to identify the most significant interactions, that were analyzed thereafter. RESULTS Mean age was 48±10 years and 72% were men. The most frequent clinical presentation was intracranial hypertension and focal deficit. Imaging revealed 88% of single lobar tumors, 96% of contrast enhancement, 52% located near eloquent areas, 48% with augmented perfusion (mean values of 300±130%) and 60% showed restricted diffusion. WHO 2016 diagnosis were glioblastoma IDH mutated (IDHmut, 16%), IDH wildtype (IDHw, 56%); anaplastic astrocytoma IDHmut (4%), IDHw (16%); diffuse astrocytoma IDHmut (4%), SEGA (4%). The genetic and metabolic profiles were normalized per sample using the total sum of all the studied variables per case. This step made the interactomic approach possible. We found no differences between the metabolic or genetic profiles of glioma grade III and IV samples. However, there was a statistical significance or near-threshold correlation between some metabolic patterns and IDH-mutation, where Alanine (4.7±1.3% IDHw vs 2.5±0.7 IDHmut, p=0.046), Glycine (2.7±0.5% vs. 1.6±0.4%, p=0.095), Glycerophosphorylcholine (3.9±0.4% vs. 6.4±0.9%, p=0.013) and Myo-inositol (4.9±1.0% vs 11.9±2.1%, p=0.004) were the most important biomarkers. Overexpression of Lactate Dehydrogenase subunit B (LDHB, 19±3% vs. 31±6%, p=0.039) and Aconitase 1 (ACO1, 0.5±0.1% vs 1.2±0.3%, p=0.08) had also a significant or near-threshold relationship with IDH-mutation. These correlations were shown as hot spots in the correlation graphs and maps. CONCLUSION These preliminary results indicate that metabolic patterns by high resolution 31P and 1H MRS could be a useful tool to improve our knowledge about glioma gene expression profiles and to identify potential biomarkers to tackle this pathology.
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Qi, Jinwei, Kang Li, Yunxia Shi, Yufei Li, Long Dong, Ling Liu, Mingyang Li, et al. "Cross-Species Comparison of Metabolomics to Decipher the Metabolic Diversity in Ten Fruits." Metabolites 11, no. 3 (March 12, 2021): 164. http://dx.doi.org/10.3390/metabo11030164.
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Fruits provide humans with multiple kinds of nutrients and protect humans against worldwide nutritional deficiency. Therefore, it is essential to understand the nutrient composition of various fruits in depth. In this study, we performed LC-MS-based non-targeted metabolomic analyses with ten kinds of fruit, including passion fruit, mango, starfruit, mangosteen, guava, mandarin orange, grape, apple, blueberry, and strawberry. In total, we detected over 2500 compounds and identified more than 300 nutrients. Although the ten fruits shared 909 common-detected compounds, each species accumulated a variety of species-specific metabolites. Additionally, metabolic profiling analyses revealed a constant variation in each metabolite’s content across the ten fruits. Moreover, we constructed a neighbor-joining tree using metabolomic data, which resembles the single-copy protein-based phylogenetic tree. This indicates that metabolome data could reflect the genetic relationship between different species. In conclusion, our work enriches knowledge on the metabolomics of fruits, and provides metabolic evidence for the genetic relationships among these fruits.
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Ackermann, Martin Daniel, John Andrew van der Poll, and Huibrecht Margaretha van der Poll. "Re-evaluating the Definition of Intelligence in Business Intelligence." GATR Journal of Management and Marketing Review 1, no. 1 (December 27, 2016): 33–44. http://dx.doi.org/10.35609/jmmr.2016.1.1(5).
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Objective - Business Intelligence has little bearing with graphs and dashboards of traditionally defined Business Intelligence. Rather it is all about experience and sound judgement of the person at the helm of the decision-making process. In line with this view, we evaluate and subsequently, reposition the current definition of Business Intelligence in the literature. Methodology/Technique - The initial development of the data, information, knowledge and wisdom (DIKW) hierarchy excluded intelligence and so it never questioned the accepted definition of Business Intelligence. The extended DIKIW hierarchy includes intelligence but we raise the question about the definition of intelligence in Business Intelligence. This paper positions the existing definition of Business Intelligence as Business Information instead, and so, it redefines traditional Business Intelligence. Findings – Applying the DIKIW hierarchy, the new definition of Business Intelligence is shown in equation as the transformation of "Business Data to Business Information to Business Knowledge to Business Intelligence to Business Wisdom". Novelty - The impact of the new definition of Business Intelligence is that it changes its meaning from one that belongs to information science into one that is a human behavioural science and profiling concept. It does not do away with the existing work in literature but it redefines Business Intelligence as belonging to the realm of Business Information. Type of Paper - Review Keywords: Business Intelligence; DIKW hierarchy; DIKIW hierarchy; Knowledge Management; Wisdom. JEL Classification: L25, M10
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Ilnitskaya, E. T., M. V. Makarkina, I. V. Stepanov, I. I. Suprun, S. V. Tokmakov, V. Ch Aiba, M. A. Avidzba, and V. K. Kotlyar. "Genetic polymorphism of local Abkhazian grape cultivars." Vavilov Journal of Genetics and Breeding 25, no. 8 (January 1, 2022): 797–804. http://dx.doi.org/10.18699/vj21.092.
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Local grape cultivars from different countries of the world are an important part of the gene pool of this culture. Of particular interest are the genotypes of the most ancient regions of viticulture. The territories of the subtropical zone of Georgia and the central part of Abkhazia belong to one of the centers of origin of the cultural grapevine. The purpose of the work was to genotype native Abkhazian grape cultivars, to study their genetic diversity based on DNA profiling data and to compare them with the genotypes of local varieties of other viticultural regions. Samples of plants were taken on the territory of the Republic of Abkhazia in private farmsteads and in the collection of the agricultural firm “Vina i Vody Abkhazii“ (“Wines and Waters of Abkhazia”). The genotyping of the Abkhazian cultivars Avasirhva, Agbizh, Azhapsh, Azhizhkvakva, Azhikvaca, Atvizh, Atyrkuazh, Achkykazh, Kachich was carried out using 14 DNA markers, 9 of which are standard microsatellite markers recommended for the identification of grape varieties. To improve our knowledge about the sizes of the identified alleles, we used the DNA of grape cultivars with a known allelic composition at the analyzed loci. Statistical analysis of the data showed that the observed heterozygosity for the analyzed loci exceeded expected values, which indicates a genetic polymorphism of the studied sample of varieties. Evaluation of genetic similarity within the analyzed group based on the results of genotyping at 14 loci showed that the cultivars Kachich and Azhapsh differed from the other Abkhazian varieties. The obtained DNA profiles of the Abkhazian cultivars were checked for compliance with DNA-fingerprints of grape varieties in the Vitis International Variety Catalogue. The Georgian varieties Azhizhkvakva and Tsitska turned out to be synonyms according to DNA profiles, two varieties from the Database (Italian Albana bianca and Georgian Ojaleshi) have differences in DNA-fingerprints from the varieties Atyrkuazh and Azhikvatsa only in one allele, respectively. When comparing the identified Abkhazian grape genotypes, their difference from the sample of Dagestan, Don, Greek, Turkish, Italian, Spanish, and French varieties and genetic similarity with the genotypes of Georgian grapes were shown.
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Mezei, Laura V., Trent E. Johnson, Steven Goodman, Cassandra Collins, and Susan E. P. Bastian. "Meeting the demands of climate change: Australian consumer acceptance and sensory profiling of red wines produced from non-traditional red grape varieties." OENO One 55, no. 2 (April 14, 2021): 29–46. http://dx.doi.org/10.20870/oeno-one.2021.55.2.4571.
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To endure the challenge of climate change, the Australian wine industry could adopt new wine grape varieties more tolerant of these pending conditions. The aims of this study were to (i) generate sensory profiles and (ii) gain knowledge about Australian wine consumers’ liking of Australian and international wines made from selected drought-resistant, red wine grape varieties not traditionally grown in Australia but better suited for a changing Australian climate. A Rate-All-That-Apply (RATA) sensory panel (n = 43) profiled 24 commercial red wines made from 9 purportedly drought-tolerant red grape varieties, plus a single example of an Australian Cabernet-Sauvignon, Grenache and Shiraz wine. A subset of 10 wines was subjected to preference trials with Australian red wine consumers (n = 113) and underwent basic chemical composition measures. Consumers liked all 10 wines, scoring them greater than 5.7 on a 9-point Likert scale. The Fine Wine Instrument (FWI) identified 3 consumer segments (Wine Enthusiasts (WE); Aspirants (ASP) and No Frills (NF)). WE liked the 2 Touriga Nacional and Nero d’Avola wines significantly more than the NF consumers and the Graciano significantly more than the ASP. Correlation tests determined that the WE segment liked wines with aromas of vanilla, sweet taste, jammy, confectionary, vanilla and woody flavours and a non-fruit after taste, and the attributes responsible for the ASP segment's liking of the wines were red colour, jammy and toasty/smoky aromas, jammy and savoury flavours and alcohol mouthfeel and non-fruity aftertaste. NF consumers liked wines with aromas of vanilla, confectionary, jammy and red fruit flavours; smooth mouthfeel and a fruity aftertaste, but disliked wines displaying aromas of cooked vegetables and savoury, bitter taste, flavours of cooked vegetables, forest floor, green pepper and herbaceous, and rough mouthfeel. WE liked wines reminiscent of Cabernet-Sauvignon, Grenache and Shiraz while the ASP and NF consumers had preferences leaning towards wines similar in style to a Shiraz and Grenache, respectively. These findings indicate to wine producers the potential of these new wines in the current Australian market and the possibility that increasing future cultivation of these varieties as a response to climate change might lead to a more sustainable wine industry in the future.
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Miliordos, Dimitrios Evangelos, Georgios Merkouropoulos, Charikleia Kogkou, Spyridon Arseniou, Anastasios Alatzas, Niki Proxenia, Polydefkis Hatzopoulos, and Yorgos Kotseridis. "Explore the Rare—Molecular Identification and Wine Evaluation of Two Autochthonous Greek Varieties: “Karnachalades” and “Bogialamades”." Plants 10, no. 8 (July 29, 2021): 1556. http://dx.doi.org/10.3390/plants10081556.
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Wines produced from autochthonous Vitis vinifera varieties have an essential financial impact on the national economy of Greece. However, scientific data regarding characteristics and quality aspects of these wines is extremely limited. The aim of the current study is to define the molecular profile and to describe chemical and sensory characteristics of the wines produced by two autochthonous red grapevine varieties—“Karnachalades” and “Bogialamades”—grown in the wider area of Soufli (Thrace, Greece). We used seven microsatellites to define the molecular profile of the two varieties, and then we compared their profile to similar molecular data from other autochthonous as well as international varieties. Grape berries were harvested at optimum technological maturity from a commercial vineyard for two consecutive vintages (2017–2018) and vilification was performed using a common vinification protocol: the 2017 vintage provided wines, from both varieties, with greater rates of phenolics and anthocyanins than 2018, whereas regarding the sensory analysis, “Bogialamades” wine provided a richer profile than “Karnachalades”. To our knowledge, this is the first study that couples both molecular profiling and exploration of the enological potential of the rare Greek varieties “Karnachalades” and “Bogialamades”; they represent two promising varieties for the production of red wines in the historic region of Thrace.
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Ramalli, Edoardo, and Barbara Pernici. "Knowledge graph embedding for experimental uncertainty estimation." Information Discovery and Delivery, February 8, 2023. http://dx.doi.org/10.1108/idd-06-2022-0060.
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Purpose Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments. Design/methodology/approach This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study. Findings The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata. Originality/value The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.
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Zhou, Jilei, Guanran Jiang, Wei Du, and Cong Han. "Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation." Electronic Commerce Research, March 3, 2022. http://dx.doi.org/10.1007/s10660-022-09541-z.
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Pu, Limeng, Manali Singha, Hsiao-Chun Wu, Costas Busch, J. Ramanujam, and Michal Brylinski. "An integrated network representation of multiple cancer-specific data for graph-based machine learning." npj Systems Biology and Applications 8, no. 1 (April 29, 2022). http://dx.doi.org/10.1038/s41540-022-00226-9.
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AbstractGenomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community at https://osf.io/dzx7b/.
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Kim, Meen Chul, Yuanyuan Feng, and Yongjun Zhu. "Mapping scientific profile and knowledge diffusion of Library Hi Tech." Library Hi Tech ahead-of-print, ahead-of-print (October 23, 2020). http://dx.doi.org/10.1108/lht-08-2019-0164.
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PurposeLibrary Hi Tech is one of the most influential journals that publish leading research in library and information science (LIS). The present study aims to understand the scholarly communication in Library Hi Tech by profiling its historic footprint, emerging trends and knowledge diffusion.Design/methodology/approachA total of 3,131 bibliographic records between 1995 and 2018 were collected from the Web of Science. Text mining, graph analysis and data visualization were used to analyze subject category assignment, domain-level citation trends, co-occurrence of keywords, keyword bursts, networks of document co-citation and landmark articles.FindingsFindings indicated that published research in the journal was largely influenced by the psychology, education and social domain as a unidisciplinary discipline. Knowledge of the journal has been disseminated into multiple domains such as LIS, computer science and education. Dominant thematic concentrations were also identified: (1) library services in academic libraries and related to digital libraries, (2) adoption of new information technologies and (3) information-seeking behavior in these contexts. Additionally, the journal has exhibited an increased research emphasis on mixed-method user-centered studies and investigations into libraries' use of new media.Originality/valueThis study provides a promising approach to understand scientific trends and the intellectual growth of journals. It also helps Library Hi Tech to become more self-explanatory with a detailed bibliometric profile and to identify future directions in editorship and readership. Finally, researchers in the community can better position their studies within the emerging trends and current challenges of the journal.
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Du, Wei, Guanran Jiang, Wei Xu, and Jian Ma. "Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)." Journal of Information Science, June 14, 2021, 016555152110239. http://dx.doi.org/10.1177/01655515211023937.
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With the rapid development of the patent marketplace, patent trading recommendation is required to mitigate the technology searching cost of patent buyers. Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company’s business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation. KBiLSTM uses knowledge graph embeddings to profile patents with rich patent information. It introduces bidirectional long short-term memory network (BiLSTM) to capture the sequential pattern in a company’s historical records. In addition, to address a company’s diverse technology interests, we design an attention mechanism to aggregate the company’s historical patents given a candidate patent. Experimental results on the United States Patent and Trademark Office (USPTO) data set show that KBiLSTM outperforms state-of-the-art baselines for patent trading recommendation in terms of F1 and normalised discounted cumulative gain (nDCG). The attention visualisation of randomly selected company intuitively demonstrates the recommendation effectiveness.
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Wang, Jiye, Chaofeng Lou, Guixia Liu, Weihua Li, Zengrui Wu, and Yun Tang. "Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening." Briefings in Bioinformatics, August 23, 2022. http://dx.doi.org/10.1093/bib/bbac351.
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Abstract Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.
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Alva Principe, Renzo Arturo, Andrea Maurino, Matteo Palmonari, Michele Ciavotta, and Blerina Spahiu. "ABSTAT-HD: a scalable tool for profiling very large knowledge graphs." VLDB Journal, September 29, 2021. http://dx.doi.org/10.1007/s00778-021-00704-2.
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AbstractProcessing large-scale and highly interconnected Knowledge Graphs (KG) is becoming crucial for many applications such as recommender systems, question answering, etc. Profiling approaches have been proposed to summarize large KGs with the aim to produce concise and meaningful representation so that they can be easily managed. However, constructing profiles and calculating several statistics such as cardinality descriptors or inferences are resource expensive. In this paper, we present ABSTAT-HD, a highly distributed profiling tool that supports users in profiling and understanding big and complex knowledge graphs. We demonstrate the impact of the new architecture of ABSTAT-HD by presenting a set of experiments that show its scalability with respect to three dimensions of the data to be processed: size, complexity and workload. The experimentation shows that our profiling framework provides informative and concise profiles, and can process and manage very large KGs.
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Ran, Congjing, Mengting He, and Le Yang. "Profiling Analysis of Web of Science Journal Articles on Intellectual Property." Data and Information Management, August 4, 2020. http://dx.doi.org/10.2478/dim-2020-0016.
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AbstractThe research on knowledge diffusion in the field of intellectual property is growing based on the current research techniques, but it is mostly focused on some of the subfields, such as patent documents and technology transfer. What is lacking in the literature is a comprehensive profile of the discipline. The paper uses bibliometric methods and visualization technologies to conduct a profiling analysis of Web of Science journal articles in the field of intellectual property from the three aspects of authorship network, geographical diffusion of collaboration, and subject cluster. Facilitated by visualization software and programming scripts, the paper presents the highly active scholars in the field through author co-citation analysis (ACA) and document co-citation analysis (DCA), the diffusion networks of collaboration through dynamic geographical graphs, and the five main clusters of disciplinary subjects, namely, Innovation, Judicature, Legislation, Information, and Market.
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Carlin, Silvia, Urska Vrhovsek, Andrea Lonardi, Lorenzo Landi, and Fulvio Mattivi. "Aromatic complexity in Verdicchio wines: a case study." OENO One 53, no. 4 (October 11, 2019). http://dx.doi.org/10.20870/oeno-one.2019.53.4.2396.
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Aim: Verdicchio is a white wine grape variety that has been cultivated for hundreds of years in the Marche region of central Italy. Verdicchio is used to produce all kinds of dry, sweet and sparkling wines, some of which can be aged for ten or more years. This study aimed to extend knowledge of the volatile profile of Verdicchio wines and the recognition and detection of odorous molecules. We considered wines produced in multiple vintages from some of the best Cru from the Marche region, in the Castelli di Jesi Classico area.Methods and results: Two data sets were considered: a vertical collection that included wines from different vintages, same variety, different production areas vinified by the same winery and a horizontal collection of wines from the 2016 harvest, considering different production areas, harvest times and clones produced by the same winery. Samples were analysed with GC×GC-ToF-MS, GC-MS-MS and GC-O. Comprehensive profiling with more than 1000 compounds allowed the wines produced in different areas to be separated. By GC-O analysis 48 main odorants were found. This survey led to the identification of 3-methyl-2,4-nonanedione (3-MND) that impart an anise note and an interesting content in methyl salicylate as a possible key odorant characteristic in Verdicchio.Conclusion: The volatile profile of different Verdicchio wines from the best production areas was investigated in detail. This work confirms that it is possible to obtain wines with very different characteristics from this variety of grapes, producing premium wines with a distinctive pattern of volatiles, reproducible across several vintages and variable depending on the different location of the vineyard and winemaking techniques. Young wines are characterised by fruity, thiolic notes, while wines aged for longer are distinguished by their norisoprenoids content and by anise and balsamic notes, which can be attributed to the presence of 3-MND and to methyl salicylate released by precursors. With a derivatisation GC-MS-MS method it was possible to quantify 3-MND in the range of 10–50 ng L−1. With the use of GC-O, 48 potentially odour-active compounds were found for this wine. Analysis of the compounds after hydrolysis confirmed that a high amount of methyl salicylate characterised this variety. Methyl salicylate has a balsamic note from wintergreen oil that is often perceived in aged Verdicchio tasting.