<i>Lite-SeqCNN:</i> A Light-Weight Deep CNN Architecture for Protein Function Prediction-Reference-Cited by-同舟云学术

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Lite-SeqCNN: A Light-Weight Deep CNN Architecture for Protein Function Prediction

Published:2023-05-01 Issue:3 Volume:20 Page:2242-2253
ISSN:1545-5963
Container-title:IEEE/ACM Transactions on Computational Biology and Bioinformatics
language:
Short-container-title:IEEE/ACM Trans. Comput. Biol. and Bioinf.

Author:

Kumar Vikash¹^ORCID,Deepak Akshay¹^ORCID,Ranjan Ashish²^ORCID,Prakash Aravind³

Affiliation:

1. Department of Computer Science & Engineering, National Institute of Technology Patna, Patna, India

2. Department of Computer Science & Engineering, ITER, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India

3. Department of Computer Science, SUNY Binghamton, Binghamton, NY, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Genetics,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/8857/10144451/10026866.pdf?arnumber=10026866

Reference39 articles.

1. Generalized cross entropy loss for training deep neural networks with noisy labels;zhang;Proc 32nd Int Adv Neural Inf Process Syst,2018

2. PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

3. Automated protein function prediction--the genomic challenge

4. Improved tools for biological sequence comparison;pearson;Proc Nat Acad Sci,1998

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