标题/作者	年份
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data Z Chen, P Zhao, F Li, TT Marquez-Lago, A Leier, J Revote, Y Zhu, ... Briefings in bioinformatics 21 (3), 1047-1057	2020
iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC ZD Su, Y Huang, ZY Zhang, YW Zhao, D Wang, W Chen, KC Chou, H Lin Bioinformatics 34 (24), 4196-4204	2018
iRNA-3typeA: identifying three types of modification at RNA’s adenosine sites W Chen, P Feng, H Yang, H Ding, H Lin, KC Chou Molecular Therapy-Nucleic Acids 11, 468-474	2018
iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach B Liu, K Li, DS Huang, KC Chou Bioinformatics 34 (22), 3835-3842	2018
iRNA (m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition W Chen, H Ding, X Zhou, H Lin, KC Chou Analytical biochemistry 561, 59-65	2018
Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species L Wei, S Luan, LAE Nagai, R Su, Q Zou Bioinformatics 35 (8), 1326-1333	2019
Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome F Li, C Li, TT Marquez-Lago, A Leier, T Akutsu, AW Purcell, A Ian Smith, ... Bioinformatics 34 (24), 4223-4231	2018
Benign and malignant classification of mammogram images based on deep learning H Li, S Zhuang, D Li, J Zhao, Y Ma Biomedical Signal Processing and Control 51, 347-354	2019
iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC B Liu, F Weng, DS Huang, KC Chou Bioinformatics 34 (18), 3086-3093	2018
SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins W Hussain, YD Khan, N Rasool, SA Khan, KC Chou Journal of theoretical biology 468, 1-11	2019
SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins W Hussain, YD Khan, N Rasool, SA Khan, KC Chou Analytical biochemistry 568, 14-23	2019
pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC X Cheng, WZ Lin, X Xiao, KC Chou Bioinformatics 35 (3), 398-406	2019
iEnhancer-5Step: identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding NQK Le, EKY Yapp, QT Ho, N Nagasundaram, YY Ou, HY Yeh Analytical biochemistry 571, 53-61	2019
iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences S Akbar, M Hayat Journal of theoretical biology 455, 205-211	2018
pLoc_bal-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC X Xiao, X Cheng, G Chen, Q Mao, KC Chou Genomics 111 (4), 886-892	2019
Advances in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs KC Chou Current medicinal chemistry 26 (26), 4918-4943	2019
MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters M Zhang, F Li, TT Marquez-Lago, A Leier, C Fan, CK Kwoh, KC Chou, ... Bioinformatics 35 (17), 2957-2965	2019
DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites F Li, J Chen, A Leier, T Marquez-Lago, Q Liu, Y Wang, J Revote, AI Smith, ... Bioinformatics 36 (4), 1057-1065	2020
Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms L Wei, J Hu, F Li, J Song, R Su, Q Zou Briefings in Bioinformatics 21 (1), 106-119	2020
Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC MF Sabooh, N Iqbal, M Khan, M Khan, HF Maqbool Journal of theoretical biology 452, 1-9	2018

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