Mini Review

Advances in deep learning-based cancer outcome prediction using multi-omics data

Andrew Zhou, Charlie Zhang and Okyaz Eminaga*

Published: 01 May, 2023 | Volume 7 - Issue 1 | Pages: 010-013

Cancer prognosis reflects a complex biological process measured by multiple types of omics data. Deep learning frameworks have been proposed to integrate multi-omics data and predict patient outcomes in different cancer types, potentially revolutionizing cancer prognosis with superior performance. This minireview summarizes the advances in the strategies for multi-omics data integration and the performance of different deep learning models in prognosis prediction of diverse cancer types using multi-omics data published in the past 18 months. The challenges and limitations of deep learning models for predicting cancer outcomes based on multi-omics data are discussed.

Read Full Article HTML DOI: 10.29328/journal.apb.1001020 Cite this Article Read Full Article PDF


Deep learning; Cancer; Outcome prediction; Multi-omics


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