Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease

Compositional framework for multitask learning in the identification of cleavage sites of HIV-1... Journal of Biomedical Informatics 102 (2020) 103376 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease Deepak Singh , Dilip Singh Sisodia, Pradeep Singh Department of Computer Science and Engineering, National Institute of Technology, Raipur, C.G, India ARTICLE INFO ABSTRACT Keywords: Inadequate patient samples and costly annotated data generations result into the smaller dataset in the bio- HIV-1 protease medical domain. Due to which the predictions with a trained model that usually reveal a single small dataset Multifactorial evolution association are fail to derive robust insights. To cope with the data sparsity, a promising strategy of combining Multitask learning data from the different related tasks is exercised in various application. Motivated by, successful work in the Multiple Kernel learning various bioinformatics application, we propose a multitask learning model based on multi-kernel that exploits Protein encoding the dependencies among various related tasks. This work aims to combine the knowledge from experimental studies of the different dataset to build stronger predictive models for HIV-1 protease cleavage sites prediction. In this study, a set of peptide data from one source is referred as ‘task’ and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Biomedical Informatics Elsevier

Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease

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Publisher
Elsevier
Copyright
Copyright © 2020 Elsevier Inc.
ISSN
1532-0464
eISSN
1532-0480
DOI
10.1016/j.jbi.2020.103376
Publisher site
See Article on Publisher Site

Abstract

Journal of Biomedical Informatics 102 (2020) 103376 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease Deepak Singh , Dilip Singh Sisodia, Pradeep Singh Department of Computer Science and Engineering, National Institute of Technology, Raipur, C.G, India ARTICLE INFO ABSTRACT Keywords: Inadequate patient samples and costly annotated data generations result into the smaller dataset in the bio- HIV-1 protease medical domain. Due to which the predictions with a trained model that usually reveal a single small dataset Multifactorial evolution association are fail to derive robust insights. To cope with the data sparsity, a promising strategy of combining Multitask learning data from the different related tasks is exercised in various application. Motivated by, successful work in the Multiple Kernel learning various bioinformatics application, we propose a multitask learning model based on multi-kernel that exploits Protein encoding the dependencies among various related tasks. This work aims to combine the knowledge from experimental studies of the different dataset to build stronger predictive models for HIV-1 protease cleavage sites prediction. In this study, a set of peptide data from one source is referred as ‘task’ and

Journal

Journal of Biomedical InformaticsElsevier

Published: Feb 1, 2020

References

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