John F. McDonald

Georgia Institute of Technology, Atlanta, GA 
"John McDonald"
Cross-listing: Computational Biology Tree

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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
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