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For: Goh WWB, Wong L. Dealing with Confounders in Omics Analysis. Trends Biotechnol 2018;36:488-98. [PMID: 29475622 DOI: 10.1016/j.tibtech.2018.01.013] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 4.8] [Reference Citation Analysis]
Number Citing Articles
1 Goh WWB, Yong CH, Wong L. Are batch effects still relevant in the age of big data? Trends in Biotechnology 2022. [DOI: 10.1016/j.tibtech.2022.02.005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
2 Li F, Zhou Y, Zhang Y, Yin J, Qiu Y, Gao J, Zhu F. POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability. Brief Bioinform 2022:bbac040. [PMID: 35183059 DOI: 10.1093/bib/bbac040] [Cited by in Crossref: 25] [Cited by in F6Publishing: 25] [Article Influence: 25.0] [Reference Citation Analysis]
3 Lim AJW, Lim LJ, Ooi BNS, Koh ET, Tan JWL, Chong SS, Khor CC, Tucker-Kellogg L, Leong KP, Lee CG; TTSH RA Study Group. Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients. EBioMedicine 2022;75:103800. [PMID: 35022146 DOI: 10.1016/j.ebiom.2021.103800] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
4 Phua S, Lim K, Goh WW. Perspectives for better batch effect correction in mass-spectrometry-based proteomics. Computational and Structural Biotechnology Journal 2022;20:4369-75. [DOI: 10.1016/j.csbj.2022.08.022] [Reference Citation Analysis]
5 Narayana JK, Mac Aogáin M, Goh WWB, Xia K, Tsaneva-Atanasova K, Chotirmall SH. Mathematical-based microbiome analytics for clinical translation. Comput Struct Biotechnol J 2021;19:6272-81. [PMID: 34900137 DOI: 10.1016/j.csbj.2021.11.029] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
6 Ho SY, Phua K, Wong L, Bin Goh WW. Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability. Patterns (N Y) 2020;1:100129. [PMID: 33294870 DOI: 10.1016/j.patter.2020.100129] [Cited by in Crossref: 32] [Cited by in F6Publishing: 22] [Article Influence: 16.0] [Reference Citation Analysis]
7 Zhao Y, Wong L, Goh WWB. How to do quantile normalization correctly for gene expression data analyses. Sci Rep 2020;10:15534. [PMID: 32968196 DOI: 10.1038/s41598-020-72664-6] [Cited by in Crossref: 24] [Cited by in F6Publishing: 25] [Article Influence: 12.0] [Reference Citation Analysis]
8 Montévil M, Acevedo N, Schaeberle CM, Bharadwaj M, Fenton SE, Soto AM. A Combined Morphometric and Statistical Approach to Assess Nonmonotonicity in the Developing Mammary Gland of Rats in the CLARITY-BPA Study. Environ Health Perspect 2020;128:57001. [PMID: 32438830 DOI: 10.1289/EHP6301] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 9.0] [Reference Citation Analysis]
9 Goh WWB, Wong L. The Birth of Bio-data Science: Trends, Expectations, and Applications. Genomics Proteomics Bioinformatics 2020;18:5-15. [PMID: 32428604 DOI: 10.1016/j.gpb.2020.01.002] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
10 Ho SY, Wong L, Goh WWB. Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy. Patterns (N Y) 2020;1:100025. [PMID: 33205097 DOI: 10.1016/j.patter.2020.100025] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
11 Taylor DL, Gough A, Schurdak ME, Vernetti L, Chennubhotla CS, Lefever D, Pei F, Faeder JR, Lezon TR, Stern AM, Bahar I. Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology. Handb Exp Pharmacol 2019;260:327-67. [PMID: 31201557 DOI: 10.1007/164_2019_239] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 5.5] [Reference Citation Analysis]
12 Giuliani A, Todde V. Big Data, Personalized Medicine and Network Pharmacology: Beyond the Current Paradigms. Approaching Complex Diseases 2020. [DOI: 10.1007/978-3-030-32857-3_5] [Reference Citation Analysis]
13 Montévil M, Acevedo N, Schaeberle CM, Bharadwaj M, Fenton SE, Soto AM. A combined morphometric and statistical approach to assess non-monotonicity in the developing mammary gland of rats in the CLARITY-BPA study.. [DOI: 10.1101/783019] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
14 Zhou L, Chi-Hau Sue A, Bin Goh WW. Examining the practical limits of batch effect-correction algorithms: When should you care about batch effects? J Genet Genomics 2019;46:433-43. [PMID: 31611172 DOI: 10.1016/j.jgg.2019.08.002] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
15 Wen Bin Goh W, Thalappilly S, Thibault G. Moving beyond the current limits of data analysis in longevity and healthy lifespan studies. Drug Discov Today 2019;24:2273-85. [PMID: 31499187 DOI: 10.1016/j.drudis.2019.08.008] [Reference Citation Analysis]
16 Zhao Y, Sue AC, Goh WWB. Deeper investigation into the utility of functional class scoring in missing protein prediction from proteomics data. J Bioinform Comput Biol 2019;17:1950013. [DOI: 10.1142/s0219720019500136] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
17 Goh WWB, Wong L. Turning straw into gold: building robustness into gene signature inference. Drug Discov Today 2019;24:31-6. [PMID: 30081096 DOI: 10.1016/j.drudis.2018.08.002] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 2.8] [Reference Citation Analysis]
18 Goh WWB, Wong L. Why breast cancer signatures are no better than random signatures explained. Drug Discov Today 2018;23:1818-23. [PMID: 29864526 DOI: 10.1016/j.drudis.2018.05.036] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]