Prediction of S-nitrosylation sites by integrating support vector machines and random forest

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

Molecular Omics, 2019, 15, 451
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Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou’s General PseAAC

Lidong Wang, Ruijun Zhang and Yashuang Mu

Journal of Theoretical Biology, 2019, 461, 51
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SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites

Hussam J. AL-barakati, Evan W. McConnell, Leslie M. Hicks, Leslie B. Poole, Robert H. Newman and Dukka B. KC

Sci Rep, 2018, 8
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In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review

Phasit Charoenkwan, Nuttapat Anuwongcharoen, Chanin Nantasenamat, Md. Mehedi Hasan and Watshara Shoombuatong

CPD, 2021, 27, 2180
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Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties

Shamima Khatun, Mehedi Hasan and Hiroyuki Kurata

FEBS Letters, 2019, 593, 3029
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Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana

Md. Parvez Mosharaf, Md. Mehedi Hassan, Fee Faysal Ahmed, Mst. Shamima Khatun, Mohammad Ali Moni and Md. Nurul Haque Mollah

Computational Biology and Chemistry, 2020, 85, 107238
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Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites

Md. Mamunur Rashid, Swakkhar Shatabda , Md. Mehedi Hasan and Hiroyuki Kurata

CG, 2020, 21, 194
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Prediction of bio-sequence modifications and the associations with diseases

Chunyan Ao, Liang Yu and Quan Zou

Briefings in Functional Genomics, 2021, 20, 1
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Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites

Md. Mehedi Hasan, Mst. Shamima Khatun and Hiroyuki Kurata

Cells, 2019, 8, 95
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Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm

Zhe Ju and Shi-Yun Wang

Journal of Theoretical Biology, 2018, 457, 6
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Investigating the Precise Identification of Citrullination Sites with High- Performance Score Metrics Using a Powerful Computation Predicting Tool

Fee Faysal Ahmed, Anamika Podder, Md. Farhad Bulbul, Md. Amzad Hossain, Mahedi Hasan, Md. Abdur Rauf Sarkar and Daijin Kim

CCHTS, 2024, 27, 1381
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Critical evaluation of web-based DNA N6-methyladenine site prediction tools

Md Mehedi Hasan, Watshara Shoombuatong, Hiroyuki Kurata and Balachandran Manavalan

Briefings in Functional Genomics, 2021, 20, 258
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ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations

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

J Comput Aided Mol Des, 2020, 34, 1229
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DeepCSO: A Deep-Learning Network Approach to Predicting Cysteine S-Sulphenylation Sites

Xiaru Lyu, Shuhao Li, Chunyang Jiang, Ningning He, Zhen Chen, Yang Zou and Lei Li

Front. Cell Dev. Biol., 2020, 8
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Review and Comparative Analysis of Machine Learning-based Predictors for Predicting and Analyzing Anti-angiogenic Peptides

Phasit Charoenkwan, Wararat Chiangjong, Md Mehedi Hasan, Chanin Nantasenamat and Watshara Shoombuatong

CMC, 2022, 29, 849
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Sul-BertGRU: an ensemble deep learning method integrating information entropy-enhanced BERT and directional multi-GRU for S-sulfhydration sites prediction

Xirun Wei, Qiao Ning, Kuiyang Che, Zhaowei Liu, Hui Li, Shikai Guo and Jonathan Wren

Bioinformatics, 2025, 41
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PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features

Mst. Shamima Khatun, Md. Mehedi Hasan and Hiroyuki Kurata

Front. Genet., 2019, 10
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Role of Machine and Deep Learning in Predicting Protein Modification Sites: Review and Future Directions

Siliang Gong and Kaiyang Qu

Information, 2025, 16, 1023
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PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

Balachandran Manavalan, Tae Hwan Shin, Myeong Ok Kim and Gwang Lee

Front. Immunol., 2018, 9
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iLBE for Computational Identification of Linear B-Cell Epitopes by Integrating Sequence and Evolutionary Features

Md. Mehedi Hasan, Mst. Shamima Khatun and Hiroyuki Kurata

Genomics, Proteomics & Bioinformatics, 2020, 18, 593
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Using deep neural networks and biological subwords to detect protein S-sulfenylation sites

Duyen Thi Do, Thanh Quynh Trang Le and Nguyen Quoc Khanh Le

Briefings in Bioinformatics, 2021, 22
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Malsite-Deep: Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy

Minghui Wang, Lili Song, Yaqun Zhang, Hongli Gao, Lu Yan and Bin Yu

Knowledge-Based Systems, 2022, 240, 108191
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ET-MSF: a model stacking framework to identify electron transport proteins

Yizheng Wang, Qingfeng Pan, Xiaobin Liu and Yijie Ding

Front. Biosci. (Landmark Ed), 2022, 27
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ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites

Yafei Zhu, Yuhai Liu, Yu Chen and Lei Li

Cells, 2022, 11, 2646
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Research progress in protein posttranslational modification site prediction

Wenying He, Leyi Wei and Quan Zou

Briefings in Functional Genomics, 2019, 18, 220
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i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome

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

International Journal of Biological Macromolecules, 2020, 157, 752
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SulSite-GTB: identification of protein S-sulfenylation sites by fusing multiple feature information and gradient tree boosting

Minghui Wang, Xiaowen Cui, Bin Yu, Cheng Chen, Qin Ma and Hongyan Zhou

Neural Comput & Applic, 2020, 32, 13843
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Improved Prediction of Protein-Protein Interaction Mapping on Homo Sapiens by Using Amino Acid Sequence Features in a Supervised Learning Framework

Md. Merajul Islam, Md. Jahangir Alam, Fee Faysal Ahmed, Md. Mehedi Hasan and Md. Nurul Haque Mollah

PPL, 2021, 28, 74
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Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

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

CG, 2020, 21, 454
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SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models

Xiaochuan Wang, Chen Li, Fuyi Li, Varun S. Sharma, Jiangning Song and Geoffrey I. Webb

BMC Bioinformatics, 2019, 20
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DNNAce: Prediction of prokaryote lysine acetylation sites through deep neural networks with multi-information fusion

Bin Yu, Zhaomin Yu, Cheng Chen, Anjun Ma, Bingqiang Liu, Baoguang Tian and Qin Ma

Chemometrics and Intelligent Laboratory Systems, 2020, 200, 103999
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AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest

Balachandran Manavalan, Tae H. Shin, Myeong O. Kim and Gwang Lee

Front. Pharmacol., 2018, 9
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A Comprehensive Comparative Review of Protein Sequence-Based Computational Prediction Models of Lysine Succinylation Sites

Samme Amena Tasmia, Md. Kaderi Kibria, Md. Ariful Islam, Mst Shamima Khatun and Md. Nurul Haque Mollah

CPPS, 2022, 23, 744
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CysModDB: a comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications

Yanzheng Meng, Lin Zhang, Laizhi Zhang, Ziyu Wang, Xuanwen Wang, Chan Li, Yu Chen, Shipeng Shang and Lei Li

Briefings in Bioinformatics, 2022, 23
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NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features

Md. Mehedi Hasan, Mst. Shamima Khatun, Md. Nurul Haque Mollah, Cao Yong and Guo Dianjing

Molecules, 2018, 23, 1667
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iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique

Muhammad Khalid Mahmood, Asma Ehsan, Yaser Daanial Khan and Kuo-Chen Chou

CG, 2020, 21, 536
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UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components

Xiaowen Cui, Zhaomin Yu, Bin Yu, Minghui Wang, Baoguang Tian and Qin Ma

Chemometrics and Intelligent Laboratory Systems, 2019, 184, 28
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IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations

Md Mehedi Hasan, Md Ashad Alam, Watshara Shoombuatong and Hiroyuki Kurata

J Comput Aided Mol Des, 2021, 35, 315
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Prediction of protein ubiquitination sites via multi-view features based on eXtreme gradient boosting classifier

Yushuang Liu, Shuping Jin, Lili Song, Yu Han and Bin Yu

Journal of Molecular Graphics and Modelling, 2021, 107, 107962
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A hybrid feature selection method for predicting lysine malonylation sites in proteins via machine learning

Hananeh Rajabiun, Mahdis MohammadHoseini, Hadi Zarezadeh and Mehdi Delkhosh

Chemometrics and Intelligent Laboratory Systems, 2022, 222, 104496
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Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information

Md. Mehedi Hasan, Md. Mamunur Rashid, Mst. Shamima Khatun and Hiroyuki Kurata

Sci Rep, 2019, 9
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Prediction of Therapeutic Peptides Using Machine Learning: Computational Models, Datasets, and Feature Encodings

Muhammad Attique, Muhammad Shoaib Farooq, Adel Khelifi and Adnan Abid

IEEE Access, 2020, 8, 148570
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i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes

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

Computational and Structural Biotechnology Journal, 2020, 18, 906
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DE-MHAIPs: Identification of SARS-CoV-2 phosphorylation sites based on differential evolution multi-feature learning and multi-head attention mechanism

Minghui Wang, Lu Yan, Jihua Jia, Jiali Lai, Hongyan Zhou and Bin Yu

Computers in Biology and Medicine, 2023, 160, 106935
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DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder

Zaheer Ullah Khan and Dechang Pi

PPL, 2021, 28, 708
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BiGRUD-SA: Protein S-sulfenylation sites prediction based on BiGRU and self-attention

Tingting Zhang, Jihua Jia, Cheng Chen, Yaqun Zhang and Bin Yu

Computers in Biology and Medicine, 2023, 163, 107145
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DeepCys: Structure‐based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains

Vamsi Nallapareddy, Shubham Bogam, Himaja Devarakonda, Shubham Paliwal and Debashree Bandyopadhyay

Proteins, 2021, 89, 745
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Prediction of protein Post-Translational Modification sites: An overviewKnorre DG, Kudryashova NV, Godovikova TS. Chemical and functional aspects of posttranslational modification of proteins. Acta Naturae. 2009; 1: 29-51. Ref.: https://goo.gl/bHviVJ Xie

Md. Mehedi Hasan and Mst. Shamima Khatun

Ann Proteom Bioinform, 2018, 2, 049
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Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier

Samme Amena Tasmia, Md. Kaderi Kibria, Khanis Farhana Tuly, Md. Ariful Islam, Mst Shamima Khatun, Md. Mehedi Hasan and Md. Nurul Haque Mollah

Sci Rep, 2022, 12
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A Systematic Review of Computational Methods for Protein Post-Translational Modification Site Prediction

Yuan-Yuan Li, Zi Liu, Xin Liu, Yi-Heng Zhu, Conghui Fang, Muhammad Arif and Wang-Ren Qiu

Arch Computat Methods Eng, 2025
DOI: 10.1007/s11831-025-10444-z

Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via CHOU’S 5-Step Rule

Ahmad Hassan Butt and Yaser Daanial Khan

Int J Pept Res Ther, 2020, 26, 1291
DOI: 10.1007/s10989-019-09931-2