Learning approaches to improve prediction of drug sensitivity in breast cancer patients

Document Type

Conference Proceeding

Publication Date

10-13-2016

Abstract

Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes. Thus, oncologists aim to identify the effective therapies for cancer patients and avoid adverse drug reactions in patients. By predicting the drug response to cancer, oncologists gain full understanding of the effective treatments on each patient, which leads to better personalized treatment. In this paper, we present three learning approaches to improve the prediction of breast cancer patients' response to chemotherapy drug: the instance selection approach, the oversampling approach, and the hybrid approach. We evaluate the performance of our approaches and compare them against the baseline approach using the Area Under the ROC Curve (AUC) on clinical trial data, in addition to testing the stability of the approaches. Our experimental results show the stability of our approaches giving the highest AUC with statistical significance.

Identifier

85009145014 (Scopus)

ISBN

[9781457702204]

Publication Title

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS

External Full Text Location

https://doi.org/10.1109/EMBC.2016.7591437

ISSN

1557170X

PubMed ID

28269014

First Page

3314

Last Page

3320

Volume

2016-October

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