research-article

Integrative Feature Ranking by Applying Deep Learning on Multi Source Genomic Data

Authors:

Fariba Khoshghalbvash,

Jean X. GaoAuthors Info & Claims

BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Pages 207 - 216

https://doi.org/10.1145/3307339.3342139

Published: 04 September 2019 Publication History

Abstract

Extracting cancer-related information from genomic data specially multi-source datasets has been an ever-growing challenge during the past years. The identification of subtype-specific genomic markers can lead to a sounder diagnosis and treatment. While several algorithms are proposed for feature extraction, to best of our knowledge, none of them consider between modality relations to discover modular disease associated biomarkers. In this paper, we represent an integrative deep learning approach to identify modular subtype-associated critical genes from three sets of input modalities for a better diagnosis of cancer subtypes. First, we train deep classifiers with different integration stages and distinct number of input modalities to predict cancer subtypes. Next, we use the optimized weight matrices of the classifier with the best performance to extract interactive top-ranked features among all input modalities. Lastly, we evaluate those ranks with other feature scoring methods according to their classification performance after feature extraction. Our results and analysis illustrate that the modular candidate biomarkers can be useful for cancer subtype detection.

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Cited By

El-Hassan A(2023)Machine Learning from Multi-omics: Applications and Data IntegrationMachine Learning Methods for Multi-Omics Data Integration10.1007/978-3-031-36502-7_2(13-21)Online publication date: 14-Nov-2023
https://doi.org/10.1007/978-3-031-36502-7_2
Alkhateeb ATabl ARueda L(2021)Deep Learning in Multi-Omics Data Integration in Cancer DiagnosticDeep Learning for Biomedical Data Analysis10.1007/978-3-030-71676-9_11(255-271)Online publication date: 17-Mar-2021
https://doi.org/10.1007/978-3-030-71676-9_11

Index Terms

Integrative Feature Ranking by Applying Deep Learning on Multi Source Genomic Data

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cover image ACM Conferences

BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 2019

716 pages

ISBN:9781450366663

DOI:10.1145/3307339

General Chairs:
Xinghua (Mindy) Shi
Temple University, USA
,
Michael Buck
University of Buffalo, USA
,
Program Chairs:
Jian Ma
Carnegie Mellon University, USA
,
Pierangelo Veltri
University Magna Graecia of Catanzaro, Italy

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 04 September 2019

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BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 7 - 10, 2019

NY, Niagara Falls, USA

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BCB '19 Paper Acceptance Rate 42 of 157 submissions, 27%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

El-Hassan A(2023)Machine Learning from Multi-omics: Applications and Data IntegrationMachine Learning Methods for Multi-Omics Data Integration10.1007/978-3-031-36502-7_2(13-21)Online publication date: 14-Nov-2023
https://doi.org/10.1007/978-3-031-36502-7_2
Alkhateeb ATabl ARueda L(2021)Deep Learning in Multi-Omics Data Integration in Cancer DiagnosticDeep Learning for Biomedical Data Analysis10.1007/978-3-030-71676-9_11(255-271)Online publication date: 17-Mar-2021
https://doi.org/10.1007/978-3-030-71676-9_11

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