Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm-Reference-Cited by-同舟云学术

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Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm

Published:2017-06-01 Issue:2 Volume:20 Page:
ISSN:1386-2073
Container-title:Combinatorial Chemistry & High Throughput Screening
language:en
Short-container-title:CCHTS

Author:

Wang ShaoPeng¹,Zhang Yu-Hang²,Cai Yu-Dong¹,Huang GuoHua³,Chen Lei⁴

Affiliation:

1. School of Life Sciences, Shanghai University, Shanghai 200444, China

2. Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

3. Department of Mathematics, Shaoyang University, Shaoyang, Hunan 422000, China

4. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Publisher

Bentham Science Publishers Ltd.

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,General Medicine

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2. New Computational Tool Based on Machine-learning Algorithms for the Identification of Rhinovirus Infection-Related Genes;Combinatorial Chemistry & High Throughput Screening;2020-01-16

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5. N-MyristoylG-PseAAC: Sequence-based Prediction of N-Myristoyl Glycine Sites in Proteins by Integration of PseAAC and Statistical Moments;Letters in Organic Chemistry;2019-02-11

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