Machine Learning Techniques for High-Throughput Structure and Function Analysis for Proteomics and Genomics

Author(s): Quan Zou

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 22 , Issue 10 , 2019

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[1]
Xu, Y.; Zhang, Y-H.; Li, J.R.; Pan, X.Y.; Huang, T.; Cai, Y.D. New computational tool based on machine-learning algorithms for the identification of rhinovirus infection-related genes. Comb. Chem. High Throughput Screen., 2019, 22(10), 665-674.
[2]
Yin, J.; Qin, Z.; Wu, K.; Zhu, Y.; Hu, L.; Kong, X. Rare germline GLMN variants identified from blue rubber bleb nevus syndrome might impact mTOR signaling. Comb. Chem. High Throughput Screen., 2019, 22(10), 675-682.
[3]
Wang, X.; Wang, S.; Song, T. A spectral rotation method with triplet periodicity property for planted motif finding problems. Comb. Chem. High Throughput Screen., 2019, 22(10), 683-693.
[4]
Zhong, W.; Zhong, B.; Zhang, H.; Chen, Z.; Chen, Y. Identification of anti-cancer peptides based on multi-classifier system. Comb. Chem. High Throughput Screen., 2019, 22(10), 694-704.
[5]
Liu, M.; Liu, G. Prediction of citrullination sites on the basis of mRMR method and SNN. Comb. Chem. High Throughput Screen., 2019, 22(10), 705-715.


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Article Details

VOLUME: 22
ISSUE: 10
Year: 2019
Page: [664 - 664]
Pages: 1
DOI: 10.2174/138620732210200110161230

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