Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors | Zendy

Dandan Zheng | Zendy; Guansong Pang | Zendy; Bo Liu | Zendy; Lihong Chen | Zendy; Jian Yang | Zendy

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Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors

Author(s) -

Dandan Zheng,

Guansong Pang,

Bo Liu,

Lihong Chen,

Jian Yang

Publication year - 2020

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/btaa230

Subject(s) - computer science , convolutional neural network , source code , artificial intelligence , binary classification , construct (python library) , f1 score , machine learning , artificial neural network , code (set theory) , deep learning , identification (biology) , pattern recognition (psychology) , data mining , support vector machine , set (abstract data type) , biology , botany , programming language , operating system

Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have a very limited number of samples available.

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