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For: Manavalan B, Subramaniyam S, Shin TH, Kim MO, Lee G. Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy. J Proteome Res 2018;17:2715-26. [PMID: 29893128 DOI: 10.1021/acs.jproteome.8b00148] [Cited by in Crossref: 95] [Cited by in F6Publishing: 77] [Article Influence: 23.8] [Reference Citation Analysis]
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8 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]
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11 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]
12 Zhang ZM, Guan ZX, Wang F, Zhang D, Ding H. Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families. Med Chem 2020;16:594-604. [PMID: 31584374 DOI: 10.2174/1573406415666191004125551] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 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]
14 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]
15 Santana K, do Nascimento LD, Lima E Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021;9:662688. [PMID: 33996755 DOI: 10.3389/fchem.2021.662688] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Liu L, Li QZ, Jin W, Lv H, Lin H. Revealing Gene Function and Transcription Relationship by Reconstructing Gene-Level Chromatin Interaction. Comput Struct Biotechnol J 2019;17:195-205. [PMID: 30828411 DOI: 10.1016/j.csbj.2019.01.011] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
17 Manavalan B, Basith S, Shin TH, Wei L, Lee G. Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation. Mol Ther Nucleic Acids 2019;16:733-44. [PMID: 31146255 DOI: 10.1016/j.omtn.2019.04.019] [Cited by in Crossref: 89] [Cited by in F6Publishing: 78] [Article Influence: 29.7] [Reference Citation Analysis]
18 Chen W, Feng P, Song X, Lv H, Lin H. iRNA-m7G: Identifying N7-methylguanosine Sites by Fusing Multiple Features. Mol Ther Nucleic Acids 2019;18:269-74. [PMID: 31581051 DOI: 10.1016/j.omtn.2019.08.022] [Cited by in Crossref: 42] [Cited by in F6Publishing: 37] [Article Influence: 14.0] [Reference Citation Analysis]
19 Nie R, Li Z, You ZH, Bao W, Li J. Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering. BMC Med Inform Decis Mak 2021;21:254. [PMID: 34461870 DOI: 10.1186/s12911-021-01616-5] [Reference Citation Analysis]
20 Ahmad A, Akbar S, Khan S, Hayat M, Ali F, Ahmed A, Tahir M. Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks. Chemometrics and Intelligent Laboratory Systems 2021;208:104214. [DOI: 10.1016/j.chemolab.2020.104214] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
21 Niu M, Li Y, Wang C, Han K. RFAmyloid: A Web Server for Predicting Amyloid Proteins. Int J Mol Sci 2018;19:E2071. [PMID: 30013015 DOI: 10.3390/ijms19072071] [Cited by in Crossref: 18] [Cited by in F6Publishing: 14] [Article Influence: 4.5] [Reference Citation Analysis]
22 Manavalan B, Basith S, Shin TH, Wei L, Lee G. mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation. Bioinformatics 2019;35:2757-65. [PMID: 30590410 DOI: 10.1093/bioinformatics/bty1047] [Cited by in Crossref: 86] [Cited by in F6Publishing: 74] [Article Influence: 43.0] [Reference Citation Analysis]
23 Zhang Y, Wang Y, Zhou W, Fan Y, Zhao J, Zhu L, Lu S, Lu T, Chen Y, Liu H. A combined drug discovery strategy based on machine learning and molecular docking. Chem Biol Drug Des 2019;93:685-99. [PMID: 30688405 DOI: 10.1111/cbdd.13494] [Cited by in Crossref: 10] [Cited by in F6Publishing: 5] [Article Influence: 3.3] [Reference Citation Analysis]
24 Lv H, Zhang Z, Li S, Tan J, Chen W, Lin H. Evaluation of different computational methods on 5-methylcytosine sites identification. Briefings in Bioinformatics 2020;21:982-95. [DOI: 10.1093/bib/bbz048] [Cited by in Crossref: 46] [Cited by in F6Publishing: 49] [Article Influence: 15.3] [Reference Citation Analysis]
25 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]
26 Nasiri F, Atanaki FF, Behrouzi S, Kavousi K, Bagheri M. CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework. ACS Omega 2021;6:19846-59. [PMID: 34368571 DOI: 10.1021/acsomega.1c02569] [Reference Citation Analysis]
27 de Oliveira ECL, Santana K, Josino L, Lima E Lima AH, de Souza de Sales Júnior C. Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space. Sci Rep 2021;11:7628. [PMID: 33828175 DOI: 10.1038/s41598-021-87134-w] [Reference Citation Analysis]
28 Dong GF, Zheng L, Huang SH, Gao J, Zuo YC. Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities. Front Genet 2021;12:669328. [PMID: 33959153 DOI: 10.3389/fgene.2021.669328] [Reference Citation Analysis]
29 Sun W, Duan M. Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machine. Energies 2019;12:277. [DOI: 10.3390/en12020277] [Cited by in Crossref: 15] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
30 Li SH, Guan ZX, Zhang D, Zhang ZM, Huang J, Yang W, Lin H. Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods. Med Chem 2020;16:605-19. [PMID: 31584379 DOI: 10.2174/1573406415666191004101913] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
31 Chen T, Wang X, Chu Y, Wang Y, Jiang M, Wei DQ, Xiong Y. T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm. Front Microbiol 2020;11:580382. [PMID: 33072049 DOI: 10.3389/fmicb.2020.580382] [Cited by in Crossref: 3] [Cited by in F6Publishing: 8] [Article Influence: 1.5] [Reference Citation Analysis]
32 Kardani K, Bolhassani A. Cppsite 2.0: An Available Database of Experimentally Validated Cell-Penetrating Peptides Predicting their Secondary and Tertiary Structures. J Mol Biol 2021;433:166703. [PMID: 33186582 DOI: 10.1016/j.jmb.2020.11.002] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
33 Malik A, Subramaniyam S, Kim CB, Manavalan B. SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information. Comput Struct Biotechnol J 2022;20:165-74. [PMID: 34976319 DOI: 10.1016/j.csbj.2021.12.014] [Reference Citation Analysis]
34 Kardani K, Bolhassani A. Exploring novel and potent cell penetrating peptides in the proteome of SARS-COV-2 using bioinformatics approaches. PLoS One 2021;16:e0247396. [PMID: 33606823 DOI: 10.1371/journal.pone.0247396] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
35 Yu GE, Shin Y, Subramaniyam S, Kang SH, Lee SM, Cho C, Lee SS, Kim CK. Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus. Sci Rep 2021;11:8019. [PMID: 33850210 DOI: 10.1038/s41598-021-87281-0] [Reference Citation Analysis]
36 Fallah Atanaki F, Behrouzi S, Ariaeenejad S, Boroomand A, Kavousi K. BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors. ACS Omega 2020;5:7290-7. [PMID: 32280870 DOI: 10.1021/acsomega.9b04119] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
37 Damiati SA, Alaofi AL, Dhar P, Alhakamy NA. Novel machine learning application for prediction of membrane insertion potential of cell-penetrating peptides. International Journal of Pharmaceutics 2019;567:118453. [DOI: 10.1016/j.ijpharm.2019.118453] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 3.3] [Reference Citation Analysis]
38 Tng SS, Le NQK, Yeh HY, Chua MCH. Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks. J Proteome Res 2021. [PMID: 34812044 DOI: 10.1021/acs.jproteome.1c00848] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Manavalan B, Govindaraj RG, Shin TH, Kim MO, Lee G. iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction. Front Immunol 2018;9:1695. [PMID: 30100904 DOI: 10.3389/fimmu.2018.01695] [Cited by in Crossref: 64] [Cited by in F6Publishing: 54] [Article Influence: 16.0] [Reference Citation Analysis]
40 Dao FY, Lv H, Wang F, Ding H. Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics. Front Genet 2018;9:613. [PMID: 30619452 DOI: 10.3389/fgene.2018.00613] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 1.8] [Reference Citation Analysis]
41 Li HF, Wang XF, Tang H. Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features. Front Bioeng Biotechnol 2020;8:183. [PMID: 32266225 DOI: 10.3389/fbioe.2020.00183] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
42 Wang J, Zhang J, Cai Y, Deng L. DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model. Int J Mol Sci 2019;20:E6046. [PMID: 31801264 DOI: 10.3390/ijms20236046] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
43 Han K, Wang M, Zhang L, Wang Y, Guo M, Zhao M, Zhao Q, Zhang Y, Zeng N, Wang C. Predicting Ion Channels Genes and Their Types With Machine Learning Techniques. Front Genet 2019;10:399. [PMID: 31130983 DOI: 10.3389/fgene.2019.00399] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.7] [Reference Citation Analysis]
44 Corrochano AR, Cal R, Kennedy K, Wall A, Murphy N, Trajkovic S, O'Callaghan S, Adelfio A, Khaldi N. Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient. Curr Res Food Sci 2021;4:224-32. [PMID: 33937870 DOI: 10.1016/j.crfs.2021.03.008] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
45 Yang H, Yang W, Dao FY, Lv H, Ding H, Chen W, Lin H. A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae. Brief Bioinform 2020;21:1568-80. [PMID: 31633777 DOI: 10.1093/bib/bbz123] [Cited by in Crossref: 46] [Cited by in F6Publishing: 38] [Article Influence: 15.3] [Reference Citation Analysis]
46 Kang MJ, Shin AY, Shin Y, Lee SA, Lee HR, Kim TD, Choi M, Koo N, Kim YM, Kyeong D, Subramaniyam S, Park EJ. Identification of transcriptome-wide, nut weight-associated SNPs in Castanea crenata. Sci Rep 2019;9:13161. [PMID: 31511588 DOI: 10.1038/s41598-019-49618-8] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
47 Chen W, Nie F, Ding H. Recent Advances of Computational Methods for Identifying Bacteriophage Virion Proteins. Protein Pept Lett 2020;27:259-64. [PMID: 30968770 DOI: 10.2174/0929866526666190410124642] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
48 Basith S, Manavalan B, Shin TH, Lee G. iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree. Comput Struct Biotechnol J 2018;16:412-20. [PMID: 30425802 DOI: 10.1016/j.csbj.2018.10.007] [Cited by in Crossref: 64] [Cited by in F6Publishing: 51] [Article Influence: 16.0] [Reference Citation Analysis]
49 Wei L, Hu J, Li F, Song J, Su R, Zou Q. Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Brief Bioinform 2018. [PMID: 30383239 DOI: 10.1093/bib/bby107] [Cited by in Crossref: 26] [Cited by in F6Publishing: 27] [Article Influence: 6.5] [Reference Citation Analysis]
50 Manavalan B, Shin TH, Kim MO, Lee G. PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions. Front Immunol 2018;9:1783. [PMID: 30108593 DOI: 10.3389/fimmu.2018.01783] [Cited by in Crossref: 55] [Cited by in F6Publishing: 45] [Article Influence: 13.8] [Reference Citation Analysis]
51 Dao FY, Lv H, Zulfiqar H, Yang H, Su W, Gao H, Ding H, Lin H. A computational platform to identify origins of replication sites in eukaryotes. Brief Bioinform 2021;22:1940-50. [PMID: 32065211 DOI: 10.1093/bib/bbaa017] [Cited by in Crossref: 35] [Cited by in F6Publishing: 31] [Article Influence: 17.5] [Reference Citation Analysis]
52 Feger G, Angelov B, Angelova A. Prediction of Amphiphilic Cell-Penetrating Peptide Building Blocks from Protein-Derived Amino Acid Sequences for Engineering of Drug Delivery Nanoassemblies. J Phys Chem B 2020;124:4069-78. [PMID: 32337991 DOI: 10.1021/acs.jpcb.0c01618] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 6.5] [Reference Citation Analysis]
53 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]
54 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]
55 Juretić D, Golemac A, Strand DE, Chung K, Ilić N, Goić-Barišić I, Pellay FX. The Spectrum of Design Solutions for Improving the Activity-Selectivity Product of Peptide Antibiotics against Multidrug-Resistant Bacteria and Prostate Cancer PC-3 Cells. Molecules 2020;25:E3526. [PMID: 32752241 DOI: 10.3390/molecules25153526] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
56 Boopathi V, Subramaniyam S, Malik A, Lee G, Manavalan B, Yang DC. mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides. Int J Mol Sci 2019;20:E1964. [PMID: 31013619 DOI: 10.3390/ijms20081964] [Cited by in Crossref: 70] [Cited by in F6Publishing: 56] [Article Influence: 23.3] [Reference Citation Analysis]
57 Su R, Hu J, Zou Q, Manavalan B, Wei L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief Bioinform 2020;21:408-20. [PMID: 30649170 DOI: 10.1093/bib/bby124] [Cited by in Crossref: 51] [Cited by in F6Publishing: 46] [Article Influence: 51.0] [Reference Citation Analysis]
58 Zhou J, Li Y, Huang W, Shi W, Qian H. Source and exploration of the peptides used to construct peptide-drug conjugates. Eur J Med Chem 2021;224:113712. [PMID: 34303870 DOI: 10.1016/j.ejmech.2021.113712] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
59 Manavalan B, Basith S, Shin TH, Choi S, Kim MO, Lee G. MLACP: machine-learning-based prediction of anticancer peptides. Oncotarget 2017;8:77121-36. [PMID: 29100375 DOI: 10.18632/oncotarget.20365] [Cited by in Crossref: 116] [Cited by in F6Publishing: 98] [Article Influence: 23.2] [Reference Citation Analysis]
60 Zhang ZM, Tan JX, Wang F, Dao FY, Zhang ZY, Lin H. Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method. Front Bioeng Biotechnol 2020;8:254. [PMID: 32292778 DOI: 10.3389/fbioe.2020.00254] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 9.5] [Reference Citation Analysis]
61 Wang Y, Wang P, Zhang J, Cui Z, Cai X, Zhang W, Chen J. A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization. Mathematics 2019;7:135. [DOI: 10.3390/math7020135] [Cited by in Crossref: 62] [Cited by in F6Publishing: 4] [Article Influence: 20.7] [Reference Citation Analysis]
62 Zhang ZY, Yang YH, Ding H, Wang D, Chen W, Lin H. Design powerful predictor for mRNA subcellular location prediction in Homo sapiens. Brief Bioinform 2021;22:526-35. [PMID: 31994694 DOI: 10.1093/bib/bbz177] [Cited by in Crossref: 34] [Cited by in F6Publishing: 37] [Article Influence: 17.0] [Reference Citation Analysis]
63 Basith S, Manavalan B, Shin TH, Lee G. SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome. Mol Ther Nucleic Acids 2019;18:131-41. [PMID: 31542696 DOI: 10.1016/j.omtn.2019.08.011] [Cited by in Crossref: 77] [Cited by in F6Publishing: 52] [Article Influence: 25.7] [Reference Citation Analysis]
64 Schissel CK, Mohapatra S, Wolfe JM, Fadzen CM, Bellovoda K, Wu CL, Wood JA, Malmberg AB, Loas A, Gómez-Bombarelli R, Pentelute BL. Deep learning to design nuclear-targeting abiotic miniproteins. Nat Chem 2021. [PMID: 34373596 DOI: 10.1038/s41557-021-00766-3] [Reference Citation Analysis]
65 Dao FY, Lv H, Su W, Sun ZJ, Huang QL, Lin H. iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network. Brief Bioinform 2021:bbab047. [PMID: 33751027 DOI: 10.1093/bib/bbab047] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
66 Manavalan B, Basith S, Shin TH, Lee DY, Wei L, Lee G. 4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-methylcytosine Sites in the Mouse Genome. Cells 2019;8:E1332. [PMID: 31661923 DOI: 10.3390/cells8111332] [Cited by in Crossref: 41] [Cited by in F6Publishing: 39] [Article Influence: 13.7] [Reference Citation Analysis]
67 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]
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