John F. McDonald
Affiliations: | Georgia Institute of Technology, Atlanta, GA |
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"John McDonald"Cross-listing: Computational Biology Tree
Children
Sign in to add trainee| Amy Csink | grad student | University of Georgia | |
| Andrew D Huang | grad student | Georgia Institute of Technology | |
| I. King Jordan | grad student | 1995-1998 | University of Georgia |
| Nathan J Bowen | grad student | 1996-2000 | University of Georgia |
| Nalini Polavarapu | grad student | 2007 | Georgia Tech |
| Shubin W. Shahab | grad student | 2011 | Georgia Tech |
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Publications
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| Housley SN, Nardelli P, Carrasco D, et al. (2020) Cancer Exacerbates Chemotherapy-Induced Sensory Neuropathy. Cancer Research |
| Zhou H, Cao H, Matyunina L, et al. (2020) MEDICASCY: A Machine Learning Approach for Predicting Small Molecule Drug Side Effects, Indications, Efficacy and Mode of Action. Molecular Pharmaceutics |
| Zhang M, Wang Y, Matyunina LV, et al. (2020) The ability of miRNAs to induce mesenchymal-to-epithelial transition (MET) in cancer cells is highly dependent upon genetic background. Cancer Letters |
| Clayton EA, Rishishwar L, Huang TC, et al. (2020) An atlas of transposable element-derived alternative splicing in cancer. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 375: 20190342 |
| Clayton EA, Khalid S, Ban D, et al. (2020) Tumor suppressor genes and allele-specific expression: mechanisms and significance. Oncotarget. 11: 462-479 |
| Zhang M, Jabbari N, Satpathy M, et al. (2019) Sequence diverse miRNAs converge to induce mesenchymal-to-epithelial transition in ovarian cancer cells through direct and indirect regulatory controls. Cancer Letters |
| Huang C, Clayton EA, Matyunina LV, et al. (2018) Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy. Scientific Reports. 8: 16444 |
| Lili LN, Huang AD, Zhang M, et al. (2018) Time-course analysis of microRNA-induced mesenchymal-to-epithelial transition underscores the complexity of the underlying molecular processes. Cancer Letters |
| Huang C, Mezencev R, McDonald JF, et al. (2017) Open source machine-learning algorithms for the prediction of optimal cancer drug therapies. Plos One. 12: e0186906 |
| Zhang M, Matyunina LV, Walker LD, et al. (2017) Evidence for the importance of post-transcriptional regulatory changes in ovarian cancer progression and the contribution of miRNAs. Scientific Reports. 7: 8171 |