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Mucaki EJ, Baranova K, Pham HQ, et al. Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning. F1000Res. 2016;5:2124doi: 10.12688/f1000research.9417.3.
Mucaki, E. J., Baranova, K., Pham, H. Q., Rezaeian, I., Angelov, D., Ngom, A., Rueda, L., & Rogan, P. K. (2016). Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning. F1000Research, 52124. https://doi.org/10.12688/f1000research.9417.3
Mucaki, Eliseos J, et al. "Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning." F1000Research vol. 5 (2016): 2124. doi: https://doi.org/10.12688/f1000research.9417.3
Mucaki EJ, Baranova K, Pham HQ, Rezaeian I, Angelov D, Ngom A, Rueda L, Rogan PK. Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning. F1000Res. 2016 Aug 31;5:2124. doi: 10.12688/f1000research.9417.3. eCollection 2016. PMID: 28620450; PMCID: PMC5461908.
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F1000Res. 2016 Aug 31;5:2124. doi: 10.12688/f1000research.9417.3. eCollection 2016.

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.

F1000Research

Eliseos J Mucaki, Katherina Baranova, Huy Q Pham, Iman Rezaeian, Dimo Angelov, Alioune Ngom, Luis Rueda, Peter K Rogan

Affiliations

  1. Deparment of Biochemistry , University of Western Ontario, London, Canada.
  2. School of Computer Science, University of Windsor, Windsor, Canada.
  3. Department of Computer Science, University of Western Ontario, London, Canada.
  4. CytoGnomix Inc, London, Canada.

PMID: 28620450 PMCID: PMC5461908 DOI: 10.12688/f1000research.9417.3

Abstract

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT  (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes 

Keywords: Gene expression signatures; breast cancer; chemotherapy resistance; hormone therapy; machine learning; random forest; support vector machine

Conflict of interest statement

Competing interests: PKR cofounded CytoGnomix. A patent application related to biologically inspired gene signatures is pending. The other authors declare that they have no competing interests.

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