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For: Laengsri V, Nantasenamat C, Schaduangrat N, Nuchnoi P, Prachayasittikul V, Shoombuatong W. TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides. Int J Mol Sci 2019;20:E2950. [PMID: 31212918 DOI: 10.3390/ijms20122950] [Cited by in Crossref: 23] [Cited by in F6Publishing: 21] [Article Influence: 7.7] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Schaduangrat N, Nantasenamat C, Prachayasittikul V, Shoombuatong W. Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation. Int J Mol Sci 2019;20:E5743. [PMID: 31731751 DOI: 10.3390/ijms20225743] [Cited by in Crossref: 32] [Cited by in F6Publishing: 27] [Article Influence: 10.7] [Reference Citation Analysis]
3 Zhang ZM, Wang JS, Zulfiqar H, Lv H, Dao FY, Lin H. Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method. Front Cell Dev Biol 2020;8:582864. [PMID: 33178697 DOI: 10.3389/fcell.2020.582864] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
4 Matsuzaka Y, Uesawa Y. Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library. Int J Mol Sci 2019;20:E4855. [PMID: 31574921 DOI: 10.3390/ijms20194855] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 2.3] [Reference Citation Analysis]
5 Hasan MM, Schaduangrat N, Basith S, Lee G, Shoombuatong W, Manavalan B. HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation. Bioinformatics 2020;36:3350-6. [PMID: 32145017 DOI: 10.1093/bioinformatics/btaa160] [Cited by in Crossref: 50] [Cited by in F6Publishing: 45] [Article Influence: 25.0] [Reference Citation Analysis]
6 Allehaibi K, Daanial Khan Y, Khan SA. iTAGPred: A Two-Level Prediction Model for Identification of Angiogenesis and Tumor Angiogenesis Biomarkers. Appl Bionics Biomech 2021;2021:2803147. [PMID: 34616486 DOI: 10.1155/2021/2803147] [Reference Citation Analysis]
7 Zulfiqar H, Khan RS, Hassan F, Hippe K, Hunt C, Ding H, Song XM, Cao R. Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. Math Biosci Eng 2021;18:3348-63. [PMID: 34198389 DOI: 10.3934/mbe.2021167] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Zhao Y, Wang S, Fei W, Feng Y, Shen L, Yang X, Wang M, Wu M. Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides. Int J Mol Sci 2021;22:5630. [PMID: 34073203 DOI: 10.3390/ijms22115630] [Reference Citation Analysis]
9 Charoenkwan P, Chiangjong W, Lee VS, Nantasenamat C, Hasan MM, Shoombuatong W. Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method. Sci Rep 2021;11:3017. [PMID: 33542286 DOI: 10.1038/s41598-021-82513-9] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
10 Charoenkwan P, Nantasenamat C, Hasan MM, Shoombuatong W. Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation. J Comput Aided Mol Des 2020;34:1105-16. [DOI: 10.1007/s10822-020-00323-z] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 8.0] [Reference Citation Analysis]
11 Liñares-Blanco J, Munteanu CR, Pazos A, Fernandez-Lozano C. Molecular docking and machine learning analysis of Abemaciclib in colon cancer. BMC Mol Cell Biol 2020;21:52. [PMID: 32640984 DOI: 10.1186/s12860-020-00295-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
12 Govindaraj RG, Subramaniyam S, Manavalan B. Extremely-randomized-tree-based Prediction of N6-Methyladenosine Sites in Saccharomyces cerevisiae. Curr Genomics 2020;21:26-33. [PMID: 32655295 DOI: 10.2174/1389202921666200219125625] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
13 Charoenkwan P, Yana J, Schaduangrat N, Nantasenamat C, Hasan MM, Shoombuatong W. iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics 2020;112:2813-22. [DOI: 10.1016/j.ygeno.2020.03.019] [Cited by in Crossref: 26] [Cited by in F6Publishing: 22] [Article Influence: 13.0] [Reference Citation Analysis]
14 Charoenkwan P, Nantasenamat C, Hasan MM, Shoombuatong W. iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation. Analytical Biochemistry 2020;599:113747. [DOI: 10.1016/j.ab.2020.113747] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 9.5] [Reference Citation Analysis]
15 Dao FY, Lv H, Yang YH, Zulfiqar H, Gao H, Lin H. Computational identification of N6-methyladenosine sites in multiple tissues of mammals. Comput Struct Biotechnol J 2020;18:1084-91. [PMID: 32435427 DOI: 10.1016/j.csbj.2020.04.015] [Cited by in Crossref: 28] [Cited by in F6Publishing: 24] [Article Influence: 14.0] [Reference Citation Analysis]
16 Balmeh N, Mahmoudi S, Fard NA. Manipulated bio antimicrobial peptides from probiotic bacteria as proposed drugs for COVID-19 disease. Inform Med Unlocked 2021;23:100515. [PMID: 33521241 DOI: 10.1016/j.imu.2021.100515] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
17 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]
18 Charoenkwan P, Schaduangrat N, Nantasenamat C, Piacham T, Shoombuatong W. iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties. Int J Mol Sci 2019;21:E75. [PMID: 31861928 DOI: 10.3390/ijms21010075] [Cited by in Crossref: 17] [Cited by in F6Publishing: 13] [Article Influence: 5.7] [Reference Citation Analysis]
19 Charoenkwan P, Yana J, Nantasenamat C, Hasan MM, Shoombuatong W. iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides. J Chem Inf Model 2020;60:6666-78. [DOI: 10.1021/acs.jcim.0c00707] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 6.5] [Reference Citation Analysis]
20 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]
21 Charoenkwan P, Kanthawong S, Schaduangrat N, Yana J, Shoombuatong W. PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method. Cells 2020;9:E353. [PMID: 32028709 DOI: 10.3390/cells9020353] [Cited by in Crossref: 20] [Cited by in F6Publishing: 17] [Article Influence: 10.0] [Reference Citation Analysis]
22 Charoenkwan P, Kanthawong S, Nantasenamat C, Hasan MM, Shoombuatong W. iAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides. Genomics 2021;113:689-98. [PMID: 33017626 DOI: 10.1016/j.ygeno.2020.09.065] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
23 He W, Jiang Y, Jin J, Li Z, Zhao J, Manavalan B, Su R, Gao X, Wei L. Accelerating bioactive peptide discovery via mutual information-based meta-learning. Brief Bioinform 2021:bbab499. [PMID: 34882225 DOI: 10.1093/bib/bbab499] [Reference Citation Analysis]
24 Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Lio' P, Shoombuatong W. iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features. Int J Mol Sci 2021;22:8958. [PMID: 34445663 DOI: 10.3390/ijms22168958] [Reference Citation Analysis]
25 Huang L, Mao F, Zang L, Zhang Y, Zhang Y, Zhang T. Estimation of hourly PM1 concentration in China and its application in population exposure analysis. Environ Pollut 2020;273:115720. [PMID: 33508630 DOI: 10.1016/j.envpol.2020.115720] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]