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Explaining Drug-Discovery Hypotheses Using Knowledge-Graph Patterns
2021
2021 IEEE International Conference on Big Data (Big Data)
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We propose explanations in the form of knowledge graph patterns, which directly relate to existing structures used by biomedical experts, as well as evaluation metrics which rely solely on existing evidence ...
present in knowledge graphs and make no domain-specific assumptions. ...
drug discovery. ...
doi:10.1109/bigdata52589.2021.9672006
fatcat:dmxjkhgum5a7lcbcyylcqc3wxy
Principles of human—computer collaboration for knowledge discovery in science
1999
Artificial Intelligence
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Our candidate for a commonality, which focuses on human factors, can be used pragmatically to evaluate and compare the designs of discovery programs that are intended to be used as collaborators by scientists ...
We characterize discovery in science as the generation of novel, interesting, plausible, and intelligible knowledge about the objects of study. ...
This article is based on a presentation at the International Congress on Discovery and Creativity held May, 1998 in Ghent, Belgium. ...
doi:10.1016/s0004-3702(98)00116-7
fatcat:cjruhp72i5cvhld747fpkgbhz4
Drug Repurposing for COVID-19 via Knowledge Graph Completion
[article]
2021
arXiv
pre-print
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Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. ...
Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. ...
We used top 50 predictions from other knowledge graph completion methods as well as the top 50 drugs generated using the discovery pattern in open discovery mode. ...
arXiv:2010.09600v2
fatcat:un74tklxczhfzmndnau7q4ql3q
Knowledge Graphs and Explainable AI in Healthcare
2022
Information
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Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions and to reduce the knowledge gap between healthcare ...
Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. ...
Then, depth-first searches were used to find every path between the drug and ADR in the graph, which can also explain new ADR discoveries. ...
doi:10.3390/info13100459
fatcat:b2jmmlcvfvelrjltnd4vifaog4
BioKG: a comprehensive, high-quality biomedical knowledge graph for AI-powered, data-driven biomedical research
[article]
2023
biorxiv/medrxiv
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data to facilitate accurate and efficient information retrieval and automated knowledge discovery (AKD). ...
acceleration in scientific discovery could be achieved through automated hypotheses generation and timely dissemination. ...
This allowed us to construct a causal knowledge graph (Supplementary Materials) for knowledge discovery applications. ...
doi:10.1101/2023.10.13.562216
pmid:38168218
pmcid:PMC10760044
fatcat:vlohuxpcwngn7h6xuoa7eq7o5y
Bisociative Knowledge Discovery for Microarray Data Analysis
2010
International Conference on Computational Creativity
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We show how enriched gene sets are found by using ontology information as background knowledge in semantic subgroup discovery. ...
The paper presents an approach to computational knowledge discovery through the mechanism of bisociation. ...
Acknowledgements The work presented in this paper was supported by the European Commission under the 7th Framework Programme FP7-ICT-2007-C FET-Open project BISON-211898, by the Slovenian Research Agency grants Knowledge ...
dblp:conf/icccrea/MozeticLPNMPGTK10
fatcat:yp7wrzzvzndglgbcigrpnqbrvi
A Short Survey of Biomedical Relation Extraction Techniques
[article]
2017
arXiv
pre-print
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Biomedical information is growing rapidly in the recent years and retrieving useful data through information extraction system is getting more attention. ...
They use linguistic patterns for identifying residues in text and then apply a graph-based method (sub-graph matching [37] ) to learn syntactic patterns corresponding to protein-residue pairs. ...
BioNoculars uses a graph-based method to construct extraction patterns for extracting protein-protein interactions. ...
arXiv:1707.05850v3
fatcat:snyvtomcxbbeplkspqaucmpely
A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions
2022
Computational and Mathematical Methods in Medicine
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chaining inference, effectively identifying facts within the graph that prove and explain the mechanisms of the drugs' interaction. ...
Given a drug pair, our framework interrogates and describes DDI mechanisms based on a knowledge graph that integrates extensive available biomedical resources through semantic web technologies and backward ...
In this phase, the ETL prepares the knowledge graph that will be used later for the inferential task. ...
doi:10.1155/2022/9093262
pmid:36035294
pmcid:PMC9402322
fatcat:6ynsdjfobvefxnkxwktdrqmrwe
A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases
2020
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. ...
To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). ...
Conclusion We describe a method for generating drug repurposing hypotheses for these rare diseases using embeddings learned from the GNBR knowledge graph. ...
pmid:31797619
pmcid:PMC6937428
fatcat:deybedz5wberxoass36dvlmchu
A Literature-Based Knowledge Graph Embedding Method for Identifying Drug RepurposingOpportunities in Rare Diseases
2020
Pacific Symposium on Biocomputing
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We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literaturederived relationships and uses link prediction to generate drug repurposing hypotheses. ...
To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). ...
Conclusion We describe a method for generating drug repurposing hypotheses for these rare diseases using embeddings learned from the GNBR knowledge graph. ...
dblp:conf/psb/SosaDGWBA20
fatcat:qiheit7txrbb7ioiveyvo7s2oa
Semantic subgroup discovery: Using ontologies in microarray data analysis
2009
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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This paper presents an approach to information fusion and creative knowledge discovery from semantically annotated knowledge sources: by using ontology information as background knowledge for semantic ...
A major challenge for next generation data mining systems is creative knowledge discovery from highly diverse and distributed data and knowledge sources. ...
We thank Igor Trajkovski for his previous work on SEGS, and Hannu Toivonen and Kimmo Kulovesi for their help when using Biomine. ...
doi:10.1109/iembs.2009.5333782
pmid:19964398
fatcat:4mc4f772r5gkjj2mmhkqtdrnze
Discovering Relations between Indirectly Connected Biomedical Concepts
[chapter]
2014
Lecture Notes in Computer Science
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This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. ...
In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation. ...
This has been done for all used knowledge sources in this work as explained in their respective description. ...
doi:10.1007/978-3-319-08590-6_11
fatcat:uwpc7cvuyvbgpajqbsvzs3geka
Discovering relations between indirectly connected biomedical concepts
2015
Journal of Biomedical Semantics
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This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. ...
In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation. ...
This has been done for all used knowledge sources in this work as explained in their respective description. ...
doi:10.1186/s13326-015-0021-5
pmid:26150906
pmcid:PMC4492092
fatcat:ywyy2adr4rgv5h2m62xpebzvgy
Knowledge-based Biomedical Data Science 2019
[article]
2019
arXiv
pre-print
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knowledge graphs. ...
Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese ...
Drug Target Discovery Using
Knowledge Graph Embeddings
55. Sadeghi A, Lehmann J. 2019. Linking Physicians to Medical Research Results via
Knowledge Graph Embeddings and Twitter. Work. Pap.
56. ...
arXiv:1910.06710v1
fatcat:kvz5k643zvhpdiq67blc2v33wi
A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases
[article]
2019
bioRxiv
pre-print
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We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. ...
To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). ...
predictions remain simply hypotheses. ...
doi:10.1101/727925
fatcat:qpubdlw775dyvnk4yyf3y447vi
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