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For: Khatun S, Hasan M, Kurata H. Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties. FEBS Lett 2019;593:3029-39. [PMID: 31297788 DOI: 10.1002/1873-3468.13536] [Cited by in Crossref: 21] [Cited by in F6Publishing: 20] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Hasan MM, Manavalan B, Khatun MS, Kurata H. Prediction of S -nitrosylation sites by integrating support vector machines and random forest. Mol Omics 2019;15:451-8. [DOI: 10.1039/c9mo00098d] [Cited by in Crossref: 28] [Cited by in F6Publishing: 13] [Article Influence: 9.3] [Reference Citation Analysis]
2 He B, Yang S, Long J, Chen X, Zhang Q, Gao H, Chen H, Huang J. TUPDB: Target-Unrelated Peptide Data Bank. Interdiscip Sci 2021;13:426-32. [PMID: 33993461 DOI: 10.1007/s12539-021-00436-5] [Reference Citation Analysis]
3 Pirtskhalava M, Amstrong AA, Grigolava M, Chubinidze M, Alimbarashvili E, Vishnepolsky B, Gabrielian A, Rosenthal A, Hurt DE, Tartakovsky M. DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Res 2021;49:D288-97. [PMID: 33151284 DOI: 10.1093/nar/gkaa991] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 16.0] [Reference Citation Analysis]
4 Meng Q, Wu Y, Sui X, Meng J, Wang T, Lin Y, Wang Z, Zhou X, Qi Y, Du J, Gao Y. POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction. Front Immunol 2020;11:02193. [PMID: 33133063 DOI: 10.3389/fimmu.2020.02193] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
5 Islam MM, Alam MJ, Ahmed FF, Hasan MM, Mollah MNH. Improved Prediction of Protein-Protein Interaction Mapping on Homo Sapiens by Using Amino Acid Sequence Features in a Supervised Learning Framework. Protein Pept Lett 2021;28:74-83. [PMID: 32520672 DOI: 10.2174/0929866527666200610141258] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Hasan MM, Manavalan B, Shoombuatong W, Khatun MS, Kurata H. i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes. Comput Struct Biotechnol J 2020;18:906-12. [PMID: 32322372 DOI: 10.1016/j.csbj.2020.04.001] [Cited by in Crossref: 22] [Cited by in F6Publishing: 19] [Article Influence: 11.0] [Reference Citation Analysis]
7 Bin Hafeez A, Jiang X, Bergen PJ, Zhu Y. Antimicrobial Peptides: An Update on Classifications and Databases. Int J Mol Sci 2021;22:11691. [PMID: 34769122 DOI: 10.3390/ijms222111691] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Auliah FN, Nilamyani AN, Shoombuatong W, Alam MA, Hasan MM, Kurata H. PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations. Int J Mol Sci 2021;22:2120. [PMID: 33672741 DOI: 10.3390/ijms22042120] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Khatun MS, Shoombuatong W, Hasan MM, Kurata H. Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction. Curr Genomics 2020;21:454-63. [PMID: 33093807 DOI: 10.2174/1389202921999200625103936] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
10 Hasan MM, Manavalan B, Khatun MS, Kurata H. i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome. Int J Biol Macromol 2020;157:752-8. [PMID: 31805335 DOI: 10.1016/j.ijbiomac.2019.12.009] [Cited by in Crossref: 39] [Cited by in F6Publishing: 35] [Article Influence: 13.0] [Reference Citation Analysis]
11 Li Y, Li X, Liu Y, Yao Y, Huang G. MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides. Pharmaceuticals 2022;15:707. [DOI: 10.3390/ph15060707] [Reference Citation Analysis]
12 Nava Lara RA, Beltrán JA, Brizuela CA, Del Rio G. Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques. Pharmaceuticals (Basel) 2020;13:E204. [PMID: 32825532 DOI: 10.3390/ph13090204] [Reference Citation Analysis]
13 Akbar S, Ahmad A, Hayat M, Rehman AU, Khan S, Ali F. iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model. Comput Biol Med 2021;137:104778. [PMID: 34481183 DOI: 10.1016/j.compbiomed.2021.104778] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Nilamyani AN, Auliah FN, Moni MA, Shoombuatong W, Hasan MM, Kurata H. PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features. Int J Mol Sci 2021;22:2704. [PMID: 33800121 DOI: 10.3390/ijms22052704] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Winkler DA. Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases. Front Chem 2021;9:614073. [PMID: 33791277 DOI: 10.3389/fchem.2021.614073] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
16 Rashid MM, Shatabda S, Hasan MM, Kurata H. Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites. Curr Genomics 2020;21:194-203. [PMID: 33071613 DOI: 10.2174/1389202921666200427210833] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
17 Hasan MM, Shoombuatong W, Kurata H, Manavalan B. Critical evaluation of web-based DNA N6-methyladenine site prediction tools. Brief Funct Genomics 2021;20:258-72. [PMID: 33491072 DOI: 10.1093/bfgp/elaa028] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
18 Hasan MM, Alam MA, Shoombuatong W, Kurata H. IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations. J Comput Aided Mol Des 2021;35:315-23. [PMID: 33392948 DOI: 10.1007/s10822-020-00368-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Mosharaf MP, Hassan MM, Ahmed FF, Khatun MS, Moni MA, Mollah MNH. Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana. Computational Biology and Chemistry 2020;85:107238. [DOI: 10.1016/j.compbiolchem.2020.107238] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
20 Hasan MM, Manavalan B, Shoombuatong W, Khatun MS, Kurata H. i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation. Plant Mol Biol 2020;103:225-34. [DOI: 10.1007/s11103-020-00988-y] [Cited by in Crossref: 24] [Cited by in F6Publishing: 21] [Article Influence: 12.0] [Reference Citation Analysis]
21 Qi L, Gao X, Pan D, Sun Y, Cai Z, Xiong Y, Dang Y. Research progress in the screening and evaluation of umami peptides. Compr Rev Food Sci Food Saf 2022. [PMID: 35201672 DOI: 10.1111/1541-4337.12916] [Reference Citation Analysis]
22 Khatun MS, Hasan MM, Shoombuatong W, Kurata H. ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations. J Comput Aided Mol Des 2020;34:1229-36. [DOI: 10.1007/s10822-020-00343-9] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
23 Basith S, Manavalan B, Hwan Shin T, Lee G. Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening. Med Res Rev 2020;40:1276-314. [DOI: 10.1002/med.21658] [Cited by in Crossref: 76] [Cited by in F6Publishing: 65] [Article Influence: 38.0] [Reference Citation Analysis]