Title: Ensemble of deep learning models to predict platinum resistance in high grade serous ovarian cancer

Authors: Kyullhee Han; Hyeonjung Ham; Se Ik Kim; Yong Sang Song; Taesung Park; TaeJin Ahn

Addresses: Interdisciplinary Course of Bioinformatics, Seoul National University, Gwanak-gu Seoul, South Korea ' Interdisciplinary Course of Bioinformatics, Seoul National University, Gwanak-gu Seoul, South Korea ' Department of Obstetrics and Gynaecology, Seoul National University College of Medicine, Gwanak-gu Seoul, South Korea ' Department of Obstetrics and Gynaecology, Seoul National University College of Medicine, Gwanak-gu Seoul, South Korea ' Department of Statistics, Seoul National University, Gwanak-gu Seoul, South Korea ' Department of Life Science, Handong Global University, Pohang, Gyeongsang, South Korea

Abstract: Deep learning has benefits to find complicated interactions between biological entities and could make useful estimation. In clinical practice, the prediction of platinum resistance in ovarian cancer is an important problem because it alters treatment options for patients and subsequently their quality of life. In this paper, Deep Neural Network (DNN) models are designed and evaluated with several feature selection approaches. Among the feature selection approaches, genes selected by group difference in gene expression showed the best performance of 0.838 in test accuracy and 0.889 in test AUC. Hybrid ensemble approaches displayed a performance of 0.903 in test accuracy and 0.869 in test AUC. An alternative hybrid ensemble model with removed partially sensitive samples displayed the performance of 0.903 in test accuracy and 0.914 in test AUC. These results suggest that a hybrid ensemble approach could help prediction of platinum resistance in ovarian cancer and subsequent treatment practice in clinics.

Keywords: ovarian cancer; deep learning; ensemble; platinum resistance; survival analysis.

DOI: 10.1504/IJDMB.2020.112851

International Journal of Data Mining and Bioinformatics, 2020 Vol.24 No.3, pp.220 - 237

Received: 22 Apr 2020
Accepted: 22 Apr 2020

Published online: 21 Nov 2020 *

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