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For: Al-Barakati H, Thapa N, Hiroto S, Roy K, Newman RH, Kc D. RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites. Comput Struct Biotechnol J 2020;18:852-60. [PMID: 32322367 DOI: 10.1016/j.csbj.2020.02.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 01/27/2020] [Accepted: 02/19/2020] [Indexed: 12/19/2022]  Open
Number Cited by Other Article(s)
1
Yang Q, Xu S, Jiang W, Meng F, Wang S, Sun Z, Chen N, Peng D, Liu J, Xing S. Systematic qualitative proteome-wide analysis of lysine malonylation profiling in Platycodon grandiflorus. Amino Acids 2025;57:9. [PMID: 39812870 PMCID: PMC11735498 DOI: 10.1007/s00726-024-03432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 11/25/2024] [Indexed: 01/16/2025]
2
Qin Z, Ren H, Zhao P, Wang K, Liu H, Miao C, Du Y, Li J, Wu L, Chen Z. Current computational tools for protein lysine acylation site prediction. Brief Bioinform 2024;25:bbae469. [PMID: 39316944 PMCID: PMC11421846 DOI: 10.1093/bib/bbae469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/20/2024] [Accepted: 09/07/2024] [Indexed: 09/26/2024]  Open
3
Shrestha P, Kandel J, Tayara H, Chong KT. Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model. Nat Commun 2024;15:6699. [PMID: 39107330 PMCID: PMC11303401 DOI: 10.1038/s41467-024-51071-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024]  Open
4
Ramazi S, Tabatabaei SAH, Khalili E, Nia AG, Motarjem K. Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences. Database (Oxford) 2024;2024:baad094. [PMID: 38245002 PMCID: PMC10799748 DOI: 10.1093/database/baad094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/22/2024]
5
Desaire H, Go EP, Hua D. Advances, obstacles, and opportunities for machine learning in proteomics. CELL REPORTS. PHYSICAL SCIENCE 2022;3:101069. [PMID: 36381226 PMCID: PMC9648337 DOI: 10.1016/j.xcrp.2022.101069] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
6
Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022;2499:285-322. [PMID: 35696087 DOI: 10.1007/978-1-0716-2317-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
7
Sorkhi AG, Pirgazi J, Ghasemi V. A hybrid feature extraction scheme for efficient malonylation site prediction. Sci Rep 2022;12:5756. [PMID: 35388017 PMCID: PMC8987080 DOI: 10.1038/s41598-022-08555-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/07/2022] [Indexed: 11/09/2022]  Open
8
Khalili E, Ramazi S, Ghanati F, Kouchaki S. Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network. Brief Bioinform 2022;23:bbac015. [PMID: 35152280 DOI: 10.1093/bib/bbac015] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/17/2021] [Accepted: 01/12/2022] [Indexed: 12/17/2023]  Open
9
Wang M, Song L, Zhang Y, Gao H, Yan L, Yu B. Malsite-Deep: Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108191] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
10
Arif M, Ahmed S, Ge F, Kabir M, Khan YD, Yu DJ, Thafar M. StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2022;220:104458. [DOI: 10.1016/j.chemolab.2021.104458] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2024]
11
Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs. Methods Mol Biol 2022;2499:155-176. [PMID: 35696080 PMCID: PMC9844236 DOI: 10.1007/978-1-0716-2317-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
12
Chaudhari M, Thapa N, Ismail H, Chopade S, Caragea D, Köhn M, Newman RH, Kc DB. DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites. Front Cell Dev Biol 2021;9:662983. [PMID: 34249915 PMCID: PMC8264445 DOI: 10.3389/fcell.2021.662983] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/20/2021] [Indexed: 11/17/2022]  Open
13
Thapa N, Chaudhari M, Iannetta AA, White C, Roy K, Newman RH, Hicks LM, Kc DB. A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites. Sci Rep 2021;11:12550. [PMID: 34131195 PMCID: PMC8206365 DOI: 10.1038/s41598-021-91840-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/28/2021] [Indexed: 11/23/2022]  Open
14
Arafat ME, Ahmad MW, Shovan S, Dehzangi A, Dipta SR, Hasan MAM, Taherzadeh G, Shatabda S, Sharma A. Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features. Genes (Basel) 2020;11:E1023. [PMID: 32878321 PMCID: PMC7565944 DOI: 10.3390/genes11091023] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/19/2020] [Accepted: 08/27/2020] [Indexed: 02/07/2023]  Open
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