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MOESM2 of Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning

Ran Wang, Shuai Li, Lixin Cheng, Man Hon Wong, Kwong Sak Leung
2019 Figshare  
Performance of decomposing the five random tensors constructed by the second strategy.  ...  Performance of decomposing the five random tensors constructed by the second strategy.  ...  Performance of using different number of latent factors is demonstrated, compared with performance of decomposing . a and b, AUC and AUPR with no additional information, respectively. c and d, AUC and  ... 
doi:10.6084/m9.figshare.11370327 fatcat:ri7dv4c3t5egpmhxzrysdt5tvy

Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning

Ran Wang, Shuai Li, Lixin Cheng, Man Hon Wong, Kwong Sak Leung
2019 BMC Bioinformatics  
The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.  ...  We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds  ...  Acknowledgements We would like to thank all of the reviewers for their comments on the manuscript. About this supplement  ... 
doi:10.1186/s12859-019-3283-6 pmid:31839008 fatcat:ugbxy2c4djhn3e5k7jgfu6e6ci

Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak

Zheng Yao Low, Isra Ahmad Farouk, Sunil Kumar Lal
2020 Viruses  
Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers.  ...  Drug repositioning results in lower overall developmental expenses and risk assessments, as the efficacy and safety of the original drug have already been well accessed and approved by regulatory authorities  ...  It involves the construction and decomposition of three-dimensional tensors which entails the associations among the trios, drugs, targets and diseases (DTD) [31] .  ... 
doi:10.3390/v12091058 pmid:32972027 pmcid:PMC7551028 fatcat:ap6aseb54fgercf4bag5vyjoti

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, Hojung Nam
2020 Biotechnology and Bioprocess Engineering  
, and drug repositioning.  ...  This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization  ...  Acknowledgments This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2020R1A2C2004628), and was supported by the Bio-Synergy Research Project  ... 
doi:10.1007/s12257-020-0049-y pmid:33437151 pmcid:PMC7790479 fatcat:wqdmkkas2nb65gy3pymlgisuwi

Universal Nature of Drug Treatment Responses in Drug-Tissue-Wide Model-Animal Experiments Using Tensor Decomposition-Based Unsupervised Feature Extraction

Yh. Taguchi, Turki Turki
2020 Frontiers in Genetics  
Here, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to the gene expression profiles of 24 mouse tissues treated with 15 drugs.  ...  For each tissue group, TD-based unsupervised FE enabled identification of a few tens to a few hundreds of genes affected by the drug treatment.  ...  ACKNOWLEDGMENTS The authors would like to thank the reviewers for very constructive comments and thoughtful suggestions.  ... 
doi:10.3389/fgene.2020.00695 pmid:32973862 pmcid:PMC7469919 fatcat:s6hlfuixnfb5zlow7lb44fglem

Predicting Potential Drug Targets Using Tensor Factorisation and Knowledge Graph Embeddings [article]

Cheng Ye, Rowan Swiers, Stephen Bonner, Ian Barrett
2021 arXiv   pre-print
In this paper, we have developed a new tensor factorisation model to predict potential drug targets (i.e.,genes or proteins) for diseases.  ...  We enriched the data with gene representations learned from a drug discovery-oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen target and dis-ease pairs  ...  ACKNOWLEDGMENTS The authors would like to thank Ufuk Kirik, Manasa Ramakrishna, Natalja Kurbatova, Elizaveta Semenova and Claus Bendtsen for help and feedback throughout the preparation of this manuscript  ... 
arXiv:2105.10578v2 fatcat:ez2hakthnndjvb5ksi6cku5ycq

A new advanced in silico drug discovery method for novel coronavirus (SARS-CoV-2) with tensor decomposition-based unsupervised feature extraction

Y-h. Taguchi, Turki Turki, Qianjun Li
2020 PLoS ONE  
Although there are a large number of candidate drug compounds that may be useful for treating COVID-19, the evaluation of these drugs is time-consuming and costly.  ...  The screen also identified ivermectin, which was first identified as an anti-parasite drug and recently the drug was included in clinical trials for SARS-CoV-2.  ...  by SBDD, it remains difficult to identify drug candidate compounds for proteins and diseases when no effective drug compounds are known.  ... 
doi:10.1371/journal.pone.0238907 pmid:32915876 fatcat:xt3dd5oiyjb2bhh4gedw6u5yem

Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data

Y-h. Taguchi
2019 BMC Bioinformatics  
Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful.  ...  In the present study, using tensor decomposition-based unsupervised feature extraction, which represents an extension of the recently proposed principal-component analysis-based feature extraction, gene  ...  [9] also integrated gene expression measurements from 100 diseases and gene expression measurements for 164 drug candidates, thereby determining predicted therapeutic potentials for these drugs.  ... 
doi:10.1186/s12859-018-2395-8 pmid:30717646 pmcid:PMC7394334 fatcat:vf54tpen4neabgu3w2sblx7vxy

A bioinformatics potpourri

Christian Schönbach, Jinyan Li, Lan Ma, Paul Horton, Muhammad Farhan Sjaugi, Shoba Ranganathan
2018 BMC Genomics  
Network consistency projection for human microbe-disease association predictions assuming that microbes with similar functions may have similar associated/not associated patterns with similar diseases  ...  Target control problem with objectives-guided optimization algorithm to identify drivers (e.g. drug target nodes or network biomarkers) controlling targets in disease-associated networks [27] .  ...  • We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:  ... 
doi:10.1186/s12864-017-4326-x pmid:29363432 pmcid:PMC5780851 fatcat:i6u2eafvzbf4lawwh2o5b7k42q

Non-Negative Matrix Factorization for Drug Repositioning: Experiments with the repoDB Dataset

Gokhan Bakal, Halil Kilicoglu, Ramakanth Kavuluru
2020 AMIA Annual Symposium Proceedings  
By using hand-curated drug-disease indications from the UMLS Metathesaurus and automatically extracted relations from the SemMedDB database, we employ non-negative matrix factorization (NMF) methods to  ...  Computational methods for drug repositioning are gaining mainstream attention with the availability of experimental gene expression datasets and manually curated relational information in knowledge bases  ...  Acknowledgements We are grateful for the support of the U.S. National Library of Medicine through NIH grant R21LM012274 and also thankful for partial support offered by the U.S.  ... 
pmid:32308816 pmcid:PMC7153111 fatcat:4utdjbg64fhyzdngpdpa7wjdlq

Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection

Y-h. Taguchi, Turki Turki
2021 IEEE Journal on Selected Topics in Signal Processing  
To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised  ...  The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating  ...  Thus, our suggestions for drug repositioning are also supported.  ... 
doi:10.1109/jstsp.2021.3061251 pmid:34812273 pmcid:PMC8545047 fatcat:vzdzod4klbhurljkyhh3cdajly

A deep learning approach to predict inter-omics interactions in multi-layer networks

Niloofar Borhani, Jafar Ghaisari, Maryam Abedi, Marzieh Kamali, Yousof Gheisari
2022 BMC Bioinformatics  
Applicability of DIDL was assessed on different networks, namely drugtarget protein, transcription factor-DNA element, and miRNA–mRNA.  ...  Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease.  ...  Drug-target interaction prediction Drug repositioning or repurposing is a promising approach in drug discovery.  ... 
doi:10.1186/s12859-022-04569-2 pmid:35081903 pmcid:PMC8793231 fatcat:7y66f4qlvvev3azpapkhlcw4gy

Polyadic Regression and its Application to Chemogenomics [chapter]

Ioakeim Perros, Fei Wang, Ping Zhang, Peter Walker, Richard Vuduc, Jyotishman Pathak, Jimeng Sun
2017 Proceedings of the 2017 SIAM International Conference on Data Mining  
In drug discovery, for instance, it is important to estimate the treatment effect of a drug on various tissue-specific diseases, as it is expressed over the available genes.  ...  Our method achieves an increase of 0.06 and 0.1 in Spearman correlation between the predicted and the actual measurement vectors, for predicting missing polyadic data and predicting polyadic data for new  ...  Acknowledgments This work was supported by NSF grants under number IIS-1418511 and CCF-1533768, research partnership between Children's Healthcare of Atlanta and the Georgia Institute of Technology, Google  ... 
doi:10.1137/1.9781611974973.9 dblp:conf/sdm/PerrosWZWVPS17 fatcat:vbmiz6ejp5fdtjkc4sz6kauvra

A unified solution for different scenarios of predicting drug-target interactions via triple matrix factorization

Jian-Yu Shi, An-Qi Zhang, Shao-Wu Zhang, Kui-Tao Mao, Siu-Ming Yiu
2018 BMC Systems Biology  
First, only a few of them can address the most difficult scenario (i.e., predicting interactions between new drugs and new targets).  ...  DTI screening considers four scenarios, depending on whether the drug is an existing or a new drug and whether the target is an existing or a new target.  ...  Availability of data and materials The dataset and codes used in this work can be download from https:// github.com/JustinShi2016/Drug-Target-Interactions/tree/master/Bioinformatics About this supplement  ... 
doi:10.1186/s12918-018-0663-x pmid:30598094 pmcid:PMC6311903 fatcat:b6auezh2yzbzhfgxjqpxtzdnfm

In silico methods for drug repurposing and pharmacology

Rachel A. Hodos, Brian A. Kidd, Khader Shameer, Ben P. Readhead, Joel T. Dudley
2016 Wiley Interdisciplinary Reviews: Systems Biology and Medicine  
We can also represent drugs using categorical metadata, such as diseases and conditions for which use of a drug is indicated, side effects, or known physiological interactions with other drugs.  ...  One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs.  ...  Acknowledgements The authors would like to acknowledge the efforts of Carmen Lopez for her design expertise in helping to create  ... 
doi:10.1002/wsbm.1337 pmid:27080087 pmcid:PMC4845762 fatcat:3c2jv246fzcmbpw2j7hqupz67q
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