Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

ABSTRACT: Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled cancer research by providing physicians with an overwhelming amount of gene-expression data from RNA-seq high-throughput platforms. In this scenario, data mining and machine learning techniques have widely contribute to gene-expression data analysis by supplying computational models to supporting decision-making on real-world data. Nevertheless, existing public gene-expression databases are characte

SUBMITTER: Lopez-Garcia G 

PROVIDER: S-EPMC7098575 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature