A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.

ABSTRACT:

Background

The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typicall

SUBMITTER: Tranchevent LC 

PROVIDER: S-EPMC6923884 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature