Filters








8,844 Hits in 3.3 sec

Discovering relations between indirectly connected biomedical concepts

Dirk Weissenborn, Michael Schroeder, George Tsatsaronis
2015 Journal of Biomedical Semantics  
This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them.  ...  Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.  ...  A trained model can in turn be used to discover a specific relation between indirectly connected concept pairs.  ... 
doi:10.1186/s13326-015-0021-5 pmid:26150906 pmcid:PMC4492092 fatcat:ywyy2adr4rgv5h2m62xpebzvgy

Discovering Relations between Indirectly Connected Biomedical Concepts [chapter]

Dirk Weissenborn, Michael Schroeder, George Tsatsaronis
2014 Lecture Notes in Computer Science  
This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them.  ...  Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.  ...  A trained model can in turn be used to discover a specific relation between indirectly connected concept pairs.  ... 
doi:10.1007/978-3-319-08590-6_11 fatcat:uwpc7cvuyvbgpajqbsvzs3geka

A method for exploring implicit concept relatedness in biomedical knowledge network

Tian Bai, Leiguang Gong, Ye Wang, Yan Wang, Casimir A. Kulikowski, Lan Huang
2016 BMC Bioinformatics  
How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major  ...  To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within  ...  information or knowledge in revealing deeper and more subtle connections between the related biomedical entities.  ... 
doi:10.1186/s12859-016-1131-5 pmid:27454167 pmcid:PMC4959351 fatcat:urmni2srifhedmn64bihxpxugq

Discovering and visualizing indirect associations between biomedical concepts

Y. Tsuruoka, M. Miwa, K. Hamamoto, J. Tsujii, S. Ananiadou
2011 Bioinformatics  
Motivation: Discovering useful associations between biomedical concepts has been one of the main goals in biomedical textmining, and understanding their biomedical contexts is crucial in the discovery  ...  The system can be used as a text search engine like PubMed with additional features to help users discover and visualize indirect associations between important biomedical concepts such as genes, diseases  ...  Discovering hidden, previously unknown and potentially useful associations between biomedical concepts such as diseases and chemical compounds from the literature is a longstanding goal in biomedical text-mining  ... 
doi:10.1093/bioinformatics/btr214 pmid:21685059 pmcid:PMC3117364 fatcat:bfgcslmmmbchzcvt3472li42eq

Discovering novel biomedical relations using ASKNet semantic networks

Brian Harrington
2011 Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies - ISABEL '11  
This paper details the ASKNet project, and explains how it could be used to develop a tool that would allow biomedical researchers to firstly identify relationships of interest between entities such as  ...  One of the greatest challenges facing the biomedical community at the moment is information overload.  ...  A simple example is given in Figure 1 , showing how relations can connect simple atomic entities (e.g., ABC Inc and Susan), or complex concepts built from simpler relationships (e.g., Bob and the concept  ... 
doi:10.1145/2093698.2093780 dblp:conf/isabel/Harrington11 fatcat:zxaz6c5ig5da5jwwq2ygfcdxqi

Using Semantic and Structural Properties of the Unified Medical Language System to Discover Potential Terminological Relationships

C. O. Patel, J. J. Cimino
2009 JAMIA Journal of the American Medical Informatics Association  
Design: The UMLS integrates knowledge from several biomedical terminologies. This knowledge can be used to discover implicit semantic relationships between concepts.  ...  The proposed relationship prediction algorithm resulted in 56% recall in top 10 results for new relationships added to subsequent versions of the UMLS between 2005 and 2007.  ...  Related Work Various methods have been developed for using the existing knowledge in the UMLS to discover novel relationships between biomedical concepts.  ... 
doi:10.1197/jamia.m2931 pmid:19261940 pmcid:PMC3202330 fatcat:e6swdxrdgfc6tbgywpli5p4f7u

A Knowledge Graph of Mechanistic Associations Between COVID-19, Diabetes Mellitus and Kidney Diseases [chapter]

Michael Barrett, Ali Daowd, Syed Sibte Raza Abidi, Samina Abidi
2021 Studies in Health Technology and Informatics  
We present a literature-based discovery approach that integrates text mining, knowledge graphs and ontologies to discover semantic associations between COVID-19 and chronic disease concepts that were represented  ...  of associated concepts as a knowledge graph, and pattern analysis to discover mechanistic associations between COVID-19, DM and CKD.  ...  LTC considers the number of intermediate concepts when A and B are 2 concepts away while PageRank is a measure of each node's connectivity in the network.  ... 
doi:10.3233/shti210187 pmid:34042772 fatcat:l2io3vfm7nbttitqp3e4hab3me

BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

Anthony ML Liekens, Jeroen De Knijf, Walter Daelemans, Bart Goethals, Peter De Rijk, Jurgen Del-Favero
2011 Genome Biology  
We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information.  ...  Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at  ...  Indeed, since functionally related concepts are connected in the graph, we may assume that concepts that are close but only indirectly related in the network may also be functionally related in the real  ... 
doi:10.1186/gb-2011-12-6-r57 pmid:21696594 pmcid:PMC3218845 fatcat:zrc6r2d6djep3bmuu7ghq5swlu

Mining Biomedical Ontologies and Data Using RDF Hypergraphs

Haishan Liu, Dejing Dou, Ruoming Jin, Paea Lependu, Nigam Shah
2013 2013 12th International Conference on Machine Learning and Applications  
In this paper, we address two interesting and related problems for mining biomedical ontologies and data: i) how to discover semantic associations with the help of formal ontologies; ii) how to identify  ...  We also show that our data mining methods can discover and suggest corrections for misinformation in biomedical ontologies. 2013 12th International Conference on Machine Learning and Applications 978-0  ...  Finally, the discovered semantic associations can be used to detect potential errors in biomedical ontologies. II. RELATED WORK A.  ... 
doi:10.1109/icmla.2013.31 dblp:conf/icmla/LiuDJLS13 fatcat:ftgbjka5lnb7vkc53utif4pkje

MOLIERE: Automatic Biomedical Hypothesis Generation System [article]

Justin Sybrandt, Michael Shtutman, Ilya Safro
2017 arXiv   pre-print
Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts.  ...  At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (  ...  The result is that a path between two indirectly related concepts will more likely include a number of abstracts.  ... 
arXiv:1702.06176v3 fatcat:552e3hdsq5cl3o4fneaknyf6ye

Choosing experiments to accelerate collective discovery

Andrey Rzhetsky, Jacob G. Foster, Ian T. Foster, James A. Evans
2015 Proceedings of the National Academy of Sciences of the United States of America  
We represent research problems as links between scientific entities in a knowledge network.  ...  Nodes in the network are scientific concepts and edges are the relations between them asserted in publications.  ...  A scientist may prefer a focus on important and/or obscure concepts; short, medium, or long walks between concepts; jumps between concepts in different components; etc. (see Figs.  ... 
doi:10.1073/pnas.1509757112 pmid:26554009 pmcid:PMC4664375 fatcat:dmu72sl7kbdrvm2shjz3gbukte

MOLIERE

Justin Sybrandt, Michael Shtutman, Ilya Safro
2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17  
Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts.  ...  At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (  ...  The result is that a path between two indirectly related concepts will more likely include a number of abstracts.  ... 
doi:10.1145/3097983.3098057 pmid:29430330 pmcid:PMC5804740 dblp:conf/kdd/SybrandtSS17 fatcat:yt3qqjxpxfdezn3trux5465jtq

Discovering synergistic qualities of published authors to enhance translational research

Nathan J Bahr, Aaron M Cohen
2008 AMIA Annual Symposium Proceedings  
Author connectivity was measured by taking the Average Path Length (APL) and cluster coefficients [1] over an OCTRIcoauthor network.  ...  A high degree of connectivity, or low APL value, would indicate that a researcher has participated in many collaborations and published many papers with other OCTRI researchers.  ...  If two authors (who have never met and have different MeSH terms) can be joined indirectly by a mutually related third concept, this would constitute evidence of potential synergy in their areas of expertise  ... 
pmid:18999089 pmcid:PMC2655954 fatcat:mhbkz4jqjbfjxk6daclijfyun4

LION LBD: a Literature-Based Discovery System for Cancer Biology

Sampo Pyysalo, Simon Baker, Imran Ali, Stefan Haselwimmer, Tejas Shah, Andrew Young, Yufan Guo, Johan Högberg, Ulla Stenius, Masashi Narita, Anna Korhonen, Russell Schwartz
2018 Bioinformatics  
The overwhelming size and rapid growth of the biomedical literature make it impossible for scientists to read all studies related to their work, potentially leading to missed connections and wasted time  ...  Evaluations of the system demonstrate its ability to identify undiscovered links and rank relevant concepts highly among potential connections.  ...  In practice, a given A can be indirectly associated with several C concepts in open discovery, and there can be connections between A and C via more than one B in closed discovery.  ... 
doi:10.1093/bioinformatics/bty845 pmid:30304355 pmcid:PMC6499247 fatcat:oswn4ppyn5aofmmtps6hhdl3za

Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

Raoul Frijters, Marianne van Vugt, Ruben Smeets, René van Schaik, Jacob de Vlieg, Wynand Alkema, Andrey Rzhetsky
2010 PLoS Computational Biology  
Apart from its use in knowledge retrieval, the co-occurrence method is also wellsuited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and  ...  A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence.  ...  Hidden relationships in literature between biomedical concepts (e.g., genes, diseases, drugs), for which A and C have no direct relationship, but are connected indirectly via B-intermediates, can be analyzed  ... 
doi:10.1371/journal.pcbi.1000943 pmid:20885778 pmcid:PMC2944780 fatcat:x2jgmftcyreklp233amsrab5qy
« Previous Showing results 1 — 15 out of 8,844 results