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Identifying RNA 5-methylcytosine sites via pseudo nucleotide compositions

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Molecular BioSystems, 2016, 12, 3307
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Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots

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Molecular BioSystems, 2016, 12, 2893
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Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC

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Journal of Theoretical Biology, 2018, 455, 319
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Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue

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DNA and Cell Biology, 2019, 38, 49
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Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses

Kuo-Chen Chou

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Analysis and Comparison of RNA Pseudouridine Site Prediction Tools

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CBIO, 2020, 15, 279
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Mol Genet Genomics, 2016, 291, 285
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Benchmark data for identifying N 6 -methyladenosine sites in the Saccharomyces cerevisiae genome

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Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression

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LOC, 2019, 16, 311
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A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition

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Journal of Theoretical Biology, 2017, 424, 49
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iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition

Wang-Ren Qiu, Shi-Yu Jiang, Zhao-Chun Xu, Xuan Xiao and Kuo-Chen Chou

Oncotarget, 2017, 8, 41178
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The Remarkable Impacts of Gordon Life Science Institute

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NS, 2021, 13, 43
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iACP: a sequence-based tool for identifying anticancer peptides

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Oncotarget, 2016, 7, 16895
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Nucleosome positioning based on generalized relative entropy

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Soft Comput, 2019, 23, 9175
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Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs

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CMC, 2019, 26, 4918
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EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features

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Sci Rep, 2016, 6
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Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

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Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

Jianhua Jia, Zi Liu, Xuan Xiao, Bingxiang Liu and Kuo-Chen Chou

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Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm

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Identify RNA-associated subcellular localizations based on multi-label learning using Chou’s 5-steps rule

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Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule

Yunyun Liang and Shengli Zhang

Biophysical Chemistry, 2019, 253, 106227
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iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach

Bin Liu, Kai Li, De-Shuang Huang, Kuo-Chen Chou and John Hancock

Bioinformatics, 2018, 34, 3835
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DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

Semmy Wellem Taju and Yu‐Yen Ou

J Comput Chem, 2019, 40, 1521
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Biological Production of (S)-acetoin: A State-of-the-Art Review

Neng-Zhong Xie, Jian-Xiu Li and Ri-Bo Huang

CTMC, 2019, 19, 2348
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Application of Euclidean distance measurement and principal component analysis for gene identification

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Gene, 2016, 583, 112
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Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods

Bin Liu, Hao Wu, Deyuan Zhang, Xiaolong Wang and Kuo-Chen Chou

Oncotarget, 2017, 8, 13338
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Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks

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iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via Chou's 5-step rule and pseudo components

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iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

Jiangning Song, Yanan Wang, Fuyi Li, Tatsuya Akutsu, Neil D Rawlings, Geoffrey I Webb and Kuo-Chen Chou

Briefings in Bioinformatics, 2019, 20, 638
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Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains isolated from pyemia

Xiaolu Bai and Xiaolin Chen

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cACP: Classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components

Shahid Akbar, Ateeq Ur Rahman, Maqsood Hayat and Mohammad Sohail

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ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

Prabina Kumar Meher, Shbana Begam, Tanmaya Kumar Sahu, Ajit Gupta, Anuj Kumar, Upendra Kumar, Atmakuri Ramakrishna Rao, Krishna Pal Singh and Om Parkash Dhankher

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Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework

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Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Wei Chen, Pengwei Xing and Quan Zou

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DPP-PseAAC: A DNA-binding protein prediction model using Chou’s general PseAAC

M. Saifur Rahman, Swakkhar Shatabda, Sanjay Saha, M. Kaykobad and M. Sohel Rahman

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iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components

Shengli Zhang, Kaiwen Yang, Yuqing Lei and Kang Song

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pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Xuan Xiao, Xiang Cheng, Shengchao Su, Qi Mao and Kuo-Chen Chou

NS, 2017, 09, 330
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Semiotic Thoughts on Biological Sequence Representations

Guillermo Restrepo

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pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC

Jianhua Jia, Liuxia Zhang, Zi Liu, Xuan Xiao and Kuo-Chen Chou

Bioinformatics, 2016, 32, 3133
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Prediction and Analysis of Protein Ubiquitin Sites in the Model Plant A. thaliana

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MCP: A multi-component learning machine to predict protein secondary structure

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In-silico analysis of gymnemagenin from Gymnema sylvestre (Retz.) R.Br. with targets related to diabetes

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IMKPse: Identification of Protein Malonylation Sites by the Key Features Into General PseAAC

Wenzheng Bao, Bin Yang, De-Shuang Huang, Dong Wang, Qi Liu, Yue-Hui Chen and Rong Bao

IEEE Access, 2019, 7, 54073
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iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components

Muhammad Tahir, Hilal Tayara and Kil To Chong

Journal of Theoretical Biology, 2019, 465, 1
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An estimator for local analysis of genome based on the minimal absent word

Lianping Yang, Xiangde Zhang, Haoyue Fu and Chenhui Yang

Journal of Theoretical Biology, 2016, 395, 23
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Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach

Baoguang Tian, Xue Wu, Cheng Chen, Wenying Qiu, Qin Ma and Bin Yu

Journal of Theoretical Biology, 2019, 462, 329
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CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction

Guiyang Zhang, Wei Luo, Jianyi Lyu, Zu-Guo Yu and Guohua Huang

Interdiscip Sci Comput Life Sci, 2022, 14, 439
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Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy

Bin Liu, Longyun Fang, Shanyi Wang, Xiaolong Wang, Hongtao Li and Kuo-Chen Chou

Journal of Theoretical Biology, 2015, 385, 153
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RBPro-RF: Use Chou’s 5-steps rule to predict RNA-binding proteins via random forest with elastic net

Xiaomeng Sun, Tingyu Jin, Cheng Chen, Xiaowen Cui, Qin Ma and Bin Yu

Chemometrics and Intelligent Laboratory Systems, 2020, 197, 103919
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iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition

Xuan Xiao, Han-Xiao Ye, Zi Liu, Jian-Hua Jia and Kuo-Chen Chou

Oncotarget, 2016, 7, 34180
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Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

Md Mehedi Hasan, Sho Tsukiyama, Jae Youl Cho, Hiroyuki Kurata, Md Ashad Alam, Xiaowen Liu, Balachandran Manavalan and Hong-Wen Deng

Molecular Therapy, 2022, 30, 2856
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Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical properties

Ya-Sen Jiao and Pu-Feng Du

Journal of Theoretical Biology, 2016, 391, 35
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An ensemble distance measure of k-mer and Natural Vector for the phylogenetic analysis of multiple-segmented viruses

Hsin-Hsiung Huang

Journal of Theoretical Biology, 2016, 398, 136
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Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets

E. Siva Sankari and D. Manimegalai

Journal of Theoretical Biology, 2017, 435, 208
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DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

Balachandran Manavalan, Tae Hwan Shin and Gwang Lee

Oncotarget, 2018, 9, 1944
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iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets

Jianhua Jia, Zi Liu, Xuan Xiao, Bingxiang Liu and Kuo-Chen Chou

Molecules, 2016, 21, 95
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Molecular classification of prostate adenocarcinoma by the integrated somatic mutation profiles and molecular network

Lei Yang, Shiyuan Wang, Meng Zhou, Xiaowen Chen, Wei Jiang, Yongchun Zuo and Yingli Lv

Sci Rep, 2017, 7
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PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest

Miao Wang, Fuyi Li, Hao Wu, Quanzhong Liu and Shuqin Li

Interdiscip Sci Comput Life Sci, 2022, 14, 697
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Predicting protein lysine methylation sites by incorporating single-residue structural features into Chou's pseudo components

Hao Qiu, Yanzhi Guo, Lezheng Yu, Xuemei Pu and Menglong Li

Chemometrics and Intelligent Laboratory Systems, 2018, 179, 31
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iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm

Omid Mahmoudi, Abdul Wahab and Kil To Chong

Genes, 2020, 11, 529
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pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset

Kuo-Chen Chou, Xiang Cheng and Xuan Xiao

Genomics, 2019, 111, 1274
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Revelation of core promoter elements and bidirectional regulation of Geminivirus genes in Escherichia coli

Hsiao-En Lin, Ying-Wen Huang, Yi-Chin Lai, Na-Sheng Lin, Yau-Heiu Hsu and Chung-Chi Hu

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Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou’s PseAAC

Zhe Ju and Jian-Jun He

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Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components

Shanxin Zhang, Weichao Zhuang and Zhenghong Xu

Analytical Biochemistry, 2018, 549, 149
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Exploring local discriminative information from evolutionary profiles for cytokine–receptor interaction prediction

Leyi Wei, Zhang Bowen, Chen Zhiyong, Xing Gao and Minghong Liao

Neurocomputing, 2016, 217, 37
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Recent Advances in Ginsenosides as Potential Therapeutics Against Breast Cancer

Yu-hang Guo, Revathimadhubala Kuruganti and Ying Gao

CTMC, 2019, 19, 2334
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Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs

Qiu-Xing Jiang

MC, 2019, 15, 443
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mRCat: A Novel CatBoost Predictor for the Binary Classification of mRNA Subcellular Localization by Fusing Large Language Model Representation and Sequence Features

Xiao Wang, Lixiang Yang and Rong Wang

Biomolecules, 2024, 14, 767
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iPhos‐PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory

Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, Dong Xu and Kuo‐Chen Chou

Molecular Informatics, 2017, 36
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Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions

Wei Zhao, Guang-Ping Li, Jun Wang, Yuan-Ke Zhou, Yang Gao and Pu-Feng Du

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Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection

Ya-Sen Jiao and Pu-Feng Du

Journal of Theoretical Biology, 2016, 402, 38
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A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome

Chowdhury Rafeed Rahman, Ruhul Amin, Swakkhar Shatabda and Md. Sadrul Islam Toaha

Sci Rep, 2021, 11
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Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC

Ernesto Contreras-Torres

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iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

Pengmian Feng, Hui Ding, Hui Yang, Wei Chen, Hao Lin and Kuo-Chen Chou

Molecular Therapy - Nucleic Acids, 2017, 7, 155
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70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

Wenying He, Cangzhi Jia, Yucong Duan and Quan Zou

BMC Syst Biol, 2018, 12
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pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning

Yutao Shao and Kuo-Chen Chou

NS, 2020, 12, 388
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Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

Bin Liu, Hao Wu and Kuo-Chen Chou

NS, 2017, 09, 67
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Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

Yiheng Wang, Tong Liu, Dong Xu, Huidong Shi, Chaoyang Zhang, Yin-Yuan Mo and Zheng Wang

Sci Rep, 2016, 6
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iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences

Wei Chen, Pengmian Feng, Hui Yang, Hui Ding, Hao Lin and Kuo-Chen Chou

Oncotarget, 2017, 8, 4208
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The Multiple Applications and Possible Mechanisms of the Hyperbaric Oxygenation Therapy

Wan Chen, Xingmei Liang, Zhihuan Nong, Yaoxuan Li, Xiaorong Pan, Chunxia Chen and Luying Huang

MC, 2019, 15, 459
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Laplacian Regularized Sparse Representation Based Classifier for Identifying DNA N4-Methylcytosine Sites via L2,1/2-Matrix Norm

Yijie Ding, Wenying He, Jijun Tang, Quan Zou and Fei Guo

IEEE/ACM Trans. Comput. Biol. and Bioinf., 2023, 20, 500
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Combined sequence and sequence-structure based methods for analyzing FGF23, CYP24A1 and VDR genes

Selvaraman Nagamani, Kh. Dhanachandra Singh and Karthikeyan Muthusamy

Meta Gene, 2016, 9, 26
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Comparison and Analysis of Computational Methods for Identifying N6-Methyladenosine Sites in Saccharomyces cerevisiae

Pengmian Feng, Lijing Feng and Chaohui Tang

CPD, 2021, 27, 1219
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Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC

Faisal Javed and Maqsood Hayat

Genomics, 2019, 111, 1325
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Characterization of proteins in different subcellular localizations for Escherichia coli K12

Qi Zhang, Shiyuan Wang, Yi Pan, Dongqing Su, Qianzi Lu, Yongchun Zuo and Lei Yang

Genomics, 2019, 111, 1134
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pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning

Xin-Xin Liu and Kuo-Chen Chou

ABB, 2020, 11, 141
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DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification

Yongxian Fan, Hui Xiong and Guicong Sun

BMC Bioinformatics, 2023, 24
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SVM‐LncRNAPro: An SVM‐Based Method for Predicting Long Noncoding RNA Promoters

Guohua Huang, Taigan Xue, Weihong Chen, Liangliang Huang, Qi Dai and JinYun Jiang

IET Systems Biology, 2025, 19
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EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features

Md. Raihan Uddin, Alok Sharma, Dewan Md Farid, Md. Mahmudur Rahman, Abdollah Dehzangi and Swakkhar Shatabda

Journal of Theoretical Biology, 2018, 443, 138
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MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou’s Five-Step Rule

Xiuquan Du, Yanyu Diao, Heng Liu and Shuo Li

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i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation

Md. Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, Mst. Shamima Khatun and Hiroyuki Kurata

Plant Mol Biol, 2020, 103, 225
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iRSpot-EL: identify recombination spots with an ensemble learning approach

Bin Liu, Shanyi Wang, Ren Long, Kuo-Chen Chou and John Hancock

Bioinformatics, 2017, 33, 35
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Non-coding RNA identification with pseudo RNA sequences and feature representation learning

Xian-gan Chen, Xiaofei Yang, Chenhong Li, Xianguang Lin and Wen Zhang

Computers in Biology and Medicine, 2023, 165, 107355
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ProtoPred: Advancing Oncological Research Through Identification of Proto-Oncogene Proteins

Sharaf J. Malebary, Rabia Khan and Yaser Daanial Khan

IEEE Access, 2021, 9, 68788
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Some illuminating remarks on molecular genetics and genomics as well as drug development

Kuo-Chen Chou

Mol Genet Genomics, 2020, 295, 261
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Predicting the Organelle Location of Noncoding RNAs Using Pseudo Nucleotide Compositions

Pengmian Feng, Jidong Zhang, Hua Tang, Wei Chen and Hao Lin

Interdiscip Sci Comput Life Sci, 2017, 9, 540
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A balanced secondary structure predictor

Md. Nasrul Islam, Sumaiya Iqbal, Ataur R. Katebi and Md. Tamjidul Hoque

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iPhos-PseEn: Identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier

Wang-Ren Qiu, Xuan Xiao, Zhao-Chun Xu and Kuo-Chen Chou

Oncotarget, 2016, 7, 51270
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4mC-RF: Improving the prediction of 4mC sites using composition and position relative features and statistical moment

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Analytical Biochemistry, 2021, 633, 114385
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Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

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Applied Sciences, 2016, 6, 406
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Characterization of archaeal promoters using explainable and web-based CNN model

Muhammad Shujaat and Shi-Qing Mao

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Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

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Use Chou's 5-steps rule to identify protein post-translational modification and its linkage to secondary metabolism during the floral development of Lonicera japonica Thunb

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