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Drug-drug interaction prediction based on co-medication patterns and graph matching [article]

Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning
2019 arXiv   pre-print
Keywords: drug-drug interaction prediction; drug combination similarity; co-medication; graph matching  ...  Graph matching methods are used in the novel kernels to measure the similarities among drug combinations, in which drug co-medication patterns are leveraged to measure single drug similarities.  ...  Figures Additional Files Additional file 1 -Drug-Drug Interaction Prediction based on Co-Medication Patterns and Graph Matching (Supplementary Materials) The additional file named as "supp.pdf" includes  ... 
arXiv:1902.08675v1 fatcat:lrmhbpxbjjhcvclosxg5gs4suq

Computerized techniques pave the way for drug-drug interaction prediction and interpretation

Reza Safdari, Reza Ferdousi, Kamal Aziziheris, Sharareh R. Niakan-Kalhori, Yadollah Omidi
2016 BioImpacts  
Acknowledgments Authors like to acknowledge the Research Center for Pharmaceutical Nanotechnology at Tabriz University of Medical Sciences for the financial support.  ...  While the inner-product based measures consider only positive matches in vectors (e.g., Jacquard, Dice), some other measures are based on both negative and positive matches.  ...  DDI alarming system Computer-based DDIs prediction of co-administrated drugs is useful in their prevention, and accordingly there What is current knowledge?  ... 
doi:10.15171/bi.2016.10 pmid:27525223 pmcid:PMC4981251 fatcat:t2x2vqncm5hzvaofabtcwdsq4y

SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations [article]

Chaoqi Yang, Cao Xiao, Fenglong Ma, Lucas Glass, Jimeng Sun
2021 arXiv   pre-print
Existing works focused on recommending drug combinations for patients with complex health conditions solely based on their electronic health records.  ...  On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches  ...  We show the Cos interacted and Cos all as well as the output DDI of two cases in Table 5 . * With mask H, Cos interacted < 0 < Cos all is observed, which indicates that the interacted drugs are less likely  ... 
arXiv:2105.02711v1 fatcat:7vj5q52s5rdrjfcoeukro2awn4

Deep Learning for Alzheimer's Disease Drug Repurposing using Knowledge Graph and Multi-level Evidence [article]

Kang-Lin Hsieh, German Plascencia-Villa, Ko-Hong Lin, George Perry, Xiaoqian Jiang, Yejin Kim
2021 medRxiv   pre-print
To address the challenge in AD drug development, we developed a multi-task machine learning pipeline to integrate a comprehensive knowledge graph on biological/pharmacological interactions and multi-level  ...  evidence on drug efficacy, to identify repurposable drugs and their combination candidates.  ...  Drug-GO interactions were established based on either a combination of curated drug-GO interactions and drug-AD interactions, or a gene-GO annotation in AD, or both.  ... 
doi:10.1101/2021.12.03.21267235 fatcat:m46fncchrvf3vfsorpflwq4rea

Constructing Knowledge Graphs and Their Biomedical Applications

David Nicholson, Casey S. Greene
2020 Computational and Structural Biotechnology Journal  
In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes.  ...  Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains.  ...  This low recall score was based on constructed patterns being too speci c to detect infrequent drug pairs.  ... 
doi:10.1016/j.csbj.2020.05.017 pmid:32637040 pmcid:PMC7327409 fatcat:eontflxz3fggdnw3jzajzr2bdu

Bridging semantics and syntax with graph algorithms—state-of-the-art of extracting biomedical relations

Yuan Luo, Özlem Uzuner, Peter Szolovits
2017 Briefings in Bioinformatics  
and drug-drug interactions.  ...  Adverse drug reaction and drug-drug interaction Adverse drug reaction (ADR) refers to unexpected injuries caused by taking a medication.  ...  [125] Stanford Grammatical rules to traverse the tree structures Yes HIVDB [154] , Re-gaDB [155] Pre-specified drug names and regular expressions Katrenko et al.  ... 
doi:10.1093/bib/bbx048 pmid:28472242 pmcid:PMC6080366 fatcat:3pu74myraffsnjttvnlgoy6kqq

Knowledge-based Biomedical Data Science 2019 [article]

Tiffany J. Callahan, Ignacio J. Tripodi Computational Bioscience Program, Department of Pharmacology, University of Colorado Denver Anschutz Medical Campus
2019 arXiv   pre-print
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  ...  Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs.  ...  Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network 54. Mohamed SK, Nounu A, Nováček V. 2019.  ... 
arXiv:1910.06710v1 fatcat:kvz5k643zvhpdiq67blc2v33wi

Drug-Drug Interaction Prediction on a Biomedical Literature Knowledge Graph

Konstantinos Bougiatiotis, Fotis Aisopos, Anastasios Nentidis, Anastasia Krithara, Georgios Paliouras
2020 Zenodo  
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 semantic paths connecting different drugs in the Graph are extracted and aggregated into feature vectors representing drug pairs.  ...  Acknowledgments This work is supported by European Union's Horizon 2020 research and innovation programme under grant agreement No. 727658, project iASiS 8 (Integration and analysis of heterogeneous big  ... 
doi:10.5281/zenodo.4006458 fatcat:5645khvcsnf77k2hgln54herpy

Drug-Drug Interactions Detection from Online Heterogeneous Healthcare Networks

Haodong Yang, Christopher C. Yang
2014 2014 IEEE International Conference on Healthcare Informatics  
Drug-drug interactions (DDIs) are a serious drug safety problem for health consumers and how to detect such interactions effectively and efficiently has been of great medical significance.  ...  Currently, methods proposed to detect DDIs are mainly based on data sources such as clinical trial data, spontaneous reporting systems, electronic medical records, and chemical/pharmacological databases  ...  classification modes such as support vector machine, naï ve Bayes and so on instead of logistic regression, as logistic regression model has limitations such as sensitivity to outliers and may not be the  ... 
doi:10.1109/ichi.2014.9 dblp:conf/ichi/YangY14 fatcat:u4un23r52nbi3mg7zbqxy63ija

Drug Repurposing for COVID-19 via Knowledge Graph Completion [article]

Rui Zhang, Dimitar Hristovski, Dalton Schutte, Andrej Kastrin, Marcelo Fiszman, Halil Kilicoglu
2021 arXiv   pre-print
Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates.  ...  These models were complemented by a discovery pattern-based approach. Results: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications.  ...  Acknowledgments We thank François-Michel Lang, Leif Neve, and Jim Mork for their assistance with processing the CORD-19 dataset with SemRep and providing updates to SemMedDB.  ... 
arXiv:2010.09600v2 fatcat:un74tklxczhfzmndnau7q4ql3q

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations [article]

Xiang Yue, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon M. Lin, Wen Zhang, Ping Zhang, Huan Sun
2019 arXiv   pre-print
We select 11 representative graph embedding methods and conduct a systematic comparison on three important biomedical link prediction tasks: drug-disease association prediction, drug-drug interaction prediction  ...  , protein-protein interaction prediction, and two node classification tasks: medical term semantic type classification, protein function prediction.  ...  Petrone, Kaushik Mani and anonymous reviewers for their helpful comments and suggestions on our work, and Ohio Supercomputer Center (OSC) (Ohio Supercomputer Center, 1987) for providing us computing resources  ... 
arXiv:1906.05017v2 fatcat:7k6vrdkwybdu7pikmxa3xrnowm

A Short Survey of Biomedical Relation Extraction Techniques [article]

Elham Shahab
2017 arXiv   pre-print
In the current research, we focus on different aspects of relation extraction techniques in biomedical domain and briefly describe the state-of-the-art for relation extraction between a variety of biological  ...  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.  ... 
arXiv:1707.05850v3 fatcat:snyvtomcxbbeplkspqaucmpely

Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods

2019 JAMIA Journal of the American Medical Informatics Association  
Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events.  ...  We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term memory (BiLSTM) networks and conditional random fields (CRF) for end-to-end extraction.  ...  FC and TTT also express their gratitude to the James Elson and the Research Impact Scholarship awards from the University of Manchester.  ... 
doi:10.1093/jamia/ocz101 pmid:31390003 pmcid:PMC6913215 fatcat:l2ciicjt6zaqniafn7ibkfef3e

Mining integrated semantic networks for drug repositioning opportunities

Joseph Mullen, Simon J. Cockell, Hannah Tipney, Peter M. Woollard, Anil Wipat
2016 PeerJ  
Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning.  ...  We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.  ...  ADDITIONAL INFORMATION AND DECLARATIONS Funding JM receives funding as a CASE student from GSK and funding from the Engineering and Physical Sciences Research Council (ref 1592752).  ... 
doi:10.7717/peerj.1558 pmid:26844016 pmcid:PMC4736989 fatcat:ljcmdqjfkrhjrn6xsxbvtajkt4

Toward a Semantic Framework for the Querying, Mining and Visualization of Cancer Microenvironment Data [chapter]

Michelangelo Ceci, Fabio Fumarola, Pietro Hiram Guzzi, Federica Mandreoli, Riccardo Martoglia, Elio Masciari, Massimo Mecella, Wilma Penzo
2012 Lecture Notes in Computer Science  
The system will be used in a pilot study on the Multiple Myeloma (MM).  ...  and visualization of the precious knowledge hidden in such a huge quantity of data.  ...  new distance measures for chemical compounds and pathologies based on protein network interaction and PUBMED abstracts.  ... 
doi:10.1007/978-3-642-32395-9_9 fatcat:wykc34mkcncwxc3vqj3fbnmvru
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