Literature DB >> 30081101

Predict effective drug combination by deep belief network and ontology fingerprints.

Guocai Chen1, Alex Tsoi2, Hua Xu1, W Jim Zheng3.   

Abstract

The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief network to predict drug synergy from gene expression, pathway and the Ontology Fingerprints-a literature derived ontological profile of genes. Using data sets provided by 2015 DREAM competition, our analysis shows that this integrative method outperforms published results from the DREAM website for 4999 drug pairs, demonstrating the feasibility of predicting drug synergy from literature and the -omics data using advanced artificial intelligence approach.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Deep belief network; Drug combination; Ontology fingerprint

Mesh:

Substances:

Year:  2018        PMID: 30081101     DOI: 10.1016/j.jbi.2018.07.024

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  14 in total

1.  Anticancer drug synergy prediction in understudied tissues using transfer learning.

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5.  OBIF: an omics-based interaction framework to reveal molecular drivers of synergy.

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Journal:  NAR Genom Bioinform       Date:  2022-04-05

6.  Machine learning methods, databases and tools for drug combination prediction.

Authors:  Lianlian Wu; Yuqi Wen; Dongjin Leng; Qinglong Zhang; Chong Dai; Zhongming Wang; Ziqi Liu; Bowei Yan; Yixin Zhang; Jing Wang; Song He; Xiaochen Bo
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Review 7.  Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.

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Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 13.994

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Review 9.  Deep Learning in Mining Biological Data.

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Journal:  Cognit Comput       Date:  2021-01-05       Impact factor: 5.418

10.  Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.

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Journal:  JMIR Mhealth Uhealth       Date:  2019-08-02       Impact factor: 4.773

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