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For: Usmani SS, Kumar R, Kumar V, Singh S, Raghava GPS. AntiTbPdb: a knowledgebase of anti-tubercular peptides. Database (Oxford) 2018;2018. [PMID: 29688365 DOI: 10.1093/database/bay025] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Kaur D, Patiyal S, Arora C, Singh R, Lodhi G, Raghava GPS. In-Silico Tool for Predicting, Scanning, and Designing Defensins. Front Immunol 2021;12:780610. [PMID: 34880873 DOI: 10.3389/fimmu.2021.780610] [Reference Citation Analysis]
2 López-García G, Dublan-García O, Arizmendi-Cotero D, Gómez Oliván LM. Antioxidant and Antimicrobial Peptides Derived from Food Proteins. Molecules 2022;27:1343. [PMID: 35209132 DOI: 10.3390/molecules27041343] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Maryam L, Usmani SS, Raghava GPS. Computational resources in the management of antibiotic resistance: Speeding up drug discovery. Drug Discov Today 2021:S1359-6446(21)00205-1. [PMID: 33892146 DOI: 10.1016/j.drudis.2021.04.016] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Jhong JH, Yao L, Pang Y, Li Z, Chung CR, Wang R, Li S, Li W, Luo M, Ma R, Huang Y, Zhu X, Zhang J, Feng H, Cheng Q, Wang C, Xi K, Wu LC, Chang TH, Horng JT, Zhu L, Chiang YC, Wang Z, Lee TY. dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data. Nucleic Acids Res 2021:gkab1080. [PMID: 34850155 DOI: 10.1093/nar/gkab1080] [Reference Citation Analysis]
5 Hashemi ZS, Zarei M, Fath MK, Ganji M, Farahani MS, Afsharnouri F, Pourzardosht N, Khalesi B, Jahangiri A, Rahbar MR, Khalili S. In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions. Front Mol Biosci 2021;8:669431. [PMID: 33996914 DOI: 10.3389/fmolb.2021.669431] [Reference Citation Analysis]
6 Usmani SS, Agrawal P, Sehgal M, Patel PK, Raghava GPS. ImmunoSPdb: an archive of immunosuppressive peptides. Database (Oxford) 2019;2019. [PMID: 30753476 DOI: 10.1093/database/baz012] [Reference Citation Analysis]
7 Aguilera-Mendoza L, Marrero-Ponce Y, Beltran JA, Tellez Ibarra R, Guillen-Ramirez HA, Brizuela CA. Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis. Bioinformatics 2019;35:4739-47. [PMID: 30994884 DOI: 10.1093/bioinformatics/btz260] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
8 Kaur D, Patiyal S, Sharma N, Usmani SS, Raghava GPS. PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands. Database (Oxford) 2019;2019:baz076. [PMID: 31250014 DOI: 10.1093/database/baz076] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 6.0] [Reference Citation Analysis]
9 Manavalan B, Basith S, Shin TH, Wei L, Lee G. AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees. Comput Struct Biotechnol J 2019;17:972-81. [PMID: 31372196 DOI: 10.1016/j.csbj.2019.06.024] [Cited by in Crossref: 51] [Cited by in F6Publishing: 42] [Article Influence: 17.0] [Reference Citation Analysis]
10 Fathi F, Ghobeh M, Tabarzad M. Anti-Microbial Peptides: Strategies of Design and Development and Their Promising Wound-Healing Activities. Mol Biol Rep. [DOI: 10.1007/s11033-022-07405-1] [Reference Citation Analysis]
11 Usmani SS, Bhalla S, Raghava GPS. Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features. Front Pharmacol 2018;9:954. [PMID: 30210341 DOI: 10.3389/fphar.2018.00954] [Cited by in Crossref: 26] [Cited by in F6Publishing: 24] [Article Influence: 6.5] [Reference Citation Analysis]
12 Kumar V, Kumar R, Agrawal P, Patiyal S, Raghava GPS. A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure. Front Pharmacol 2020;11:54. [PMID: 32153395 DOI: 10.3389/fphar.2020.00054] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
13 Vishnepolsky B, Grigolava M, Managadze G, Gabrielian A, Rosenthal A, Hurt DE, Tartakovsky M, Pirtskhalava M. Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction. Brief Bioinform 2022:bbac233. [PMID: 35724561 DOI: 10.1093/bib/bbac233] [Reference Citation Analysis]
14 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]
15 Quiroz C, Saavedra YB, Armijo-Galdames B, Amado-Hinojosa J, Olivera-Nappa Á, Sanchez-Daza A, Medina-Ortiz D. Peptipedia: a user-friendly web application and a comprehensive database for peptide research supported by Machine Learning approach. Database (Oxford) 2021;2021:baab055. [PMID: 34478499 DOI: 10.1093/database/baab055] [Reference Citation Analysis]
16 Yathursan S, Wiles S, Read H, Sarojini V. A review on anti-tuberculosis peptides: Impact of peptide structure on anti-tuberculosis activity. J Pept Sci 2019;25:e3213. [PMID: 31515916 DOI: 10.1002/psc.3213] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
17 Nagpal G, Usmani SS, Raghava GPS. A Web Resource for Designing Subunit Vaccine Against Major Pathogenic Species of Bacteria. Front Immunol 2018;9:2280. [PMID: 30356876 DOI: 10.3389/fimmu.2018.02280] [Cited by in Crossref: 22] [Cited by in F6Publishing: 20] [Article Influence: 5.5] [Reference Citation Analysis]
18 Dua K, Rapalli VK, Shukla SD, Singhvi G, Shastri MD, Chellappan DK, Satija S, Mehta M, Gulati M, Pinto TDJA, Gupta G, Hansbro PM. Multi-drug resistant Mycobacterium tuberculosis & oxidative stress complexity: Emerging need for novel drug delivery approaches. Biomedicine & Pharmacotherapy 2018;107:1218-29. [DOI: 10.1016/j.biopha.2018.08.101] [Cited by in Crossref: 42] [Cited by in F6Publishing: 31] [Article Influence: 10.5] [Reference Citation Analysis]
19 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]
20 Jain P, Tiwari AK, Som T. Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection. Soft Comput 2021;25:3065-86. [DOI: 10.1007/s00500-020-05363-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
21 Aljanaby AAJ, Al-faham QMH, Aljanaby IAJ, Hasan TH. Epidemiological study of Mycobacterium Tuberculosis in Baghdad Governorate, Iraq. Gene Reports 2022;26:101467. [DOI: 10.1016/j.genrep.2021.101467] [Reference Citation Analysis]
22 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]
23 Oliveira GS, Costa RP, Gomes P, Gomes MS, Silva T, Teixeira C. Antimicrobial Peptides as Potential Anti-Tubercular Leads: A Concise Review. Pharmaceuticals (Basel) 2021;14:323. [PMID: 33918182 DOI: 10.3390/ph14040323] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
24 Shi G, Kang X, Dong F, Liu Y, Zhu N, Hu Y, Xu H, Lao X, Zheng H. DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides. Nucleic Acids Res 2021:gkab651. [PMID: 34390348 DOI: 10.1093/nar/gkab651] [Reference Citation Analysis]