Feature selection may improve deep neural networks for the bioinformatics problems | Zendy

Zheng Chen | Zendy; Meng Pang | Zendy; Zixin Zhao | Zendy; Shuainan Li | Zendy; Rui Miao | Zendy; Yifan Zhang | Zendy; Xiaoyue Feng | Zendy; Xin Feng | Zendy; Yexian Zhang | Zendy; Meiyu Duan | Zendy; Lan Huang | Zendy; Fengfeng Zhou | Zendy

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Feature selection may improve deep neural networks for the bioinformatics problems

Author(s) -

Zheng Chen,

Meng Pang,

Zixin Zhao,

Shuainan Li,

Rui Miao,

Yifan Zhang,

Xiaoyue Feng,

Xin Feng,

Yexian Zhang,

Meiyu Duan,

Lan Huang,

Fengfeng Zhou

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz763

Subject(s) - computer science , feature selection , artificial intelligence , machine learning , artificial neural network , convolutional neural network , python (programming language) , feature (linguistics) , support vector machine , pattern recognition (psychology) , selection (genetic algorithm) , binary classification , deep learning , data mining , philosophy , linguistics , operating system

Deep neural network (DNN) algorithms were utilized in predicting various biomedical phenotypes recently, and demonstrated very good prediction performances without selecting features. This study proposed a hypothesis that the DNN models may be further improved by feature selection algorithms.

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