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Discovering Drug-Target Interaction Knowledge from Biomedical Literature
[article]
2021
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As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from biomedical literature, which are usually triplets about drugs, targets and their interaction ...
The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. ...
of discovering drug, target and their interaction from the literature, which is an important topic. ...
doi:10.48550/arxiv.2109.13187
fatcat:xaq2ctjugjhlbhm7sfifgtzh3i
COVID-19 knowledge graph from semantic integration of biomedical literature and databases
2021
Bioinformatics
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Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis, and treatment ...
We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator, and SemRep with relevant biological databases and formalized the knowledge in a standardized ...
The need for computational approaches and tools that can distill biomedical knowledge from literature and integrate it with relevant information from curated biological databases is essential to gain insight ...
doi:10.1093/bioinformatics/btab694
pmid:34613368
pmcid:PMC8513397
fatcat:ferel5cds5axdmy535q54aovz4
Building a Knowledge Graph Representing Causal Associations Between Risk Factors and Incidence of Breast Cancer
[chapter]
2021
Studies in Health Technology and Informatics
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Our approach analyzes biomedical text (from PubMed abstracts), Semantic Medline database, evidence-based semantic associations, literature-based discovery, and graph database to discover associations between ...
The intent of this work is to offer an interactive knowledge synthesis platform to empower health-information-seeking individuals to learn about and mitigate modifiable risk factors. ...
In biomedical research, LBD has been applied to discover disease candidate genes, determine adverse drug reactions, detect drug-drug interactions , and find cancer treatment pathways [3] . ...
doi:10.3233/shti210267
pmid:34042671
fatcat:qdlg6ufwj5bjfmpahu2aesvrwm
Towards a Knowledge Graph of Combined Drug Therapies using Semantic Predications from Biomedical Literature (Preprint)
2020
JMIR Medical Informatics
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This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical ...
Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. ...
Propose an automated algorithm to discover knowledge about combination drug therapies based on semantic predications extracted from conclusive claims in biomedical literature 2. ...
doi:10.2196/18323
pmid:32343247
fatcat:ypyk76bwfzdrxjv5f2bsxis7fi
Review of Drug Repositioning Approaches and Resources
2018
International Journal of Biological Sciences
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Different from traditional drug development strategies, the strategy is efficient, economical and riskless. ...
Finally, challenges and opportunities in drug repositioning are discussed from multiple perspectives, including technology, commercial models, patents and investment. ...
[35] developed an approach to building dependency graph networks through extracting sentences with genes, drugs and phenotypes from biomedical literature. ...
doi:10.7150/ijbs.24612
pmid:30123072
pmcid:PMC6097480
fatcat:yvyuqvjcivd3bhkej6gyfrb42u
Biomedical text mining and its applications in cancer research
2013
Journal of Biomedical Informatics
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The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. ...
With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. ...
Discovering knowledge from biomedical text is a process with the aims to find answers for biomedical questions, such as identifying new drug targets or novel cancer diagnostic biomarkers. ...
doi:10.1016/j.jbi.2012.10.007
pmid:23159498
fatcat:xd7j77sbwfhklkat6tael64lbq
Drug-Drug Interaction Prediction on a Biomedical Literature Knowledge Graph
2020
Zenodo
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In this paper, we present an approach discovering probable drug-to-drug interactions, through the generation of a Knowledge Graph from disease-specific literature. ...
A classifier is trained on known interactions, extracted from a manually curated drug database used as a golden standard, and discovers new possible interacting pairs. ...
The latter publication presents SemaTyP, a method for discovering drug-disease relations based on a literature Knowledge Graph. ...
doi:10.5281/zenodo.4006458
fatcat:5645khvcsnf77k2hgln54herpy
Generation and application of drug indication inference models using typed network motif comparison analysis
2013
BMC Medical Informatics and Decision Making
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As the amount of publicly available biomedical data increases, discovering hidden knowledge from biomedical data (i.e., Undiscovered Public Knowledge (UPK) proposed by Swanson) became an important research ...
TNMCA is a powerful inference algorithm for multi-level biomedical interaction data as TNMs depend on the different types of entities and relations. ...
Conclusions Studies on drug repositioning have recently been rigorously carried out, and it is a difficult challenge to infer novel drug indications from a large amount of multi-level biomedical interaction ...
doi:10.1186/1472-6947-13-s1-s2
pmid:23566076
pmcid:PMC3618246
fatcat:aqz7mdh6ljerrjsxewkw36xy6e
Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
2014
Cancer Informatics
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In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. ...
We used the semantic relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations using SemRep, to extract potential relationships using knowledge of cancer drugs ...
We have recently exploited SemMedDB as a structured knowledge resource for discovering drug-drug interactions in clinical data. 14 discovery patterns. ...
doi:10.4137/cin.s13889
pmid:25392688
pmcid:PMC4216049
fatcat:m2gal3ngxjdevogl2wbjyixwoy
Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts
2009
PLoS Computational Biology
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We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation ...
We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. ...
the actual drug-target knowledge. ...
doi:10.1371/journal.pcbi.1000450
pmid:19649302
pmcid:PMC2709445
fatcat:3fr3zhwflbdd7fdnev3iit3lry
Text Mining Supporting Search for Knowledge Discovery in Diabetes
2012
Current Cardiovascular Risk Reports
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Due to increasing specialization, silo effects and literature deluge, researchers are struggling to draw out general truths and to generate testable hypotheses. ...
working in a myriad of diverse areas including: basic research, translational medicine, clinical care, clinical trials, epidemiology, public health, clinical guideline development, evaluation of new drugs ...
In this way, FACTA+ potentially discovers new knowledge, hidden in the sea of literature. ...
doi:10.1007/s12170-012-0288-3
fatcat:4hhb4akdgrakbc4sfgmr3f3tcu
Learning to Discover Medicines
[article]
2022
arXiv
pre-print
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where we discuss the construction and reasoning over biomedical knowledge graphs. ...
We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven ...
Automating biomedical knowledge graph construction Biomedical knowledge graph is constructed using existing databases or a rich source of data from biomedical publications. ...
arXiv:2202.07096v1
fatcat:u77zls6hezffbkmm3zy2rhnueu
BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation
2011
Genome Biology
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We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge ...
We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. ...
items from the biomedical literature. ...
doi:10.1186/gb-2011-12-6-r57
pmid:21696594
pmcid:PMC3218845
fatcat:zrc6r2d6djep3bmuu7ghq5swlu
Discovering context-specific relationships from biological literature by using multi-level context terms
2012
BMC Medical Informatics and Decision Making
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Methods: We propose 3 steps to discover meaningful hidden relationships between drugs and diseases: 1) multi-level (gene, drug, disease, symptom) entity recognition, 2) interaction extraction (drug-gene ...
, gene-disease) from literature, 3) context vector based similarity score calculation. ...
Weeber [4] attempted to discover novel relationships between drugs and diseases in the biomedical literature. ...
doi:10.1186/1472-6947-12-s1-s1
pmid:22595086
pmcid:PMC3339396
fatcat:eafp4eqaqfg25cgjh3utb2bkba
Metabolomic and Network Analysis of Pharmacotherapies for Sensorineural Hearing Loss
2014
Otology and Neurotology
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Methods: Drugs that have shown efficacy in treating mammalian SNHL were identified through PubMed literature searches. ...
The top 3 most interconnected molecules and drugs (i.e., the hubs) within the generated networks were considered important targets for the treatment of SNHL. ...
interactions, extracted from the scientific literature (7, 14) . ...
doi:10.1097/mao.0000000000000254
pmid:24335929
fatcat:fsdg63moavcn5aakcs7v7c2di4
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