Review: Deep Learning-Based Survival Analysis of Omics and Clinicopathological Data-Reference-Cited by-同舟云学术

Review: Deep Learning-Based Survival Analysis of Omics and Clinicopathological Data

Published:2024-05-16 Issue:3 Volume:9 Page:59
ISSN:2411-5134
Container-title:Inventions
language:en
Short-container-title:Inventions

Author:

Sidorova Julia¹,Lozano Juan Jose¹

Affiliation:

1. Bioinformatics Platform, Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Monforte de Lemos 3-5, Pabellón nº 11, 8029 Madrid, Spain

Abstract

The 2017–2024 period has been prolific in the area of the algorithms for deep-based survival analysis. We have searched the answers to the following three questions. (1) Is there a new “gold standard” already in clinical data analysis? (2) Does the DL component lead to a notably improved performance? (3) Are there tangible benefits of deep-based survival that are not directly attainable with non-deep methods? We have analyzed and compared the selected influential algorithms devised for two types of input: clinicopathological (a small set of numeric, binary and categorical) and omics data (numeric and extremely high dimensional with a pronounced p >> n complication).

Publisher

MDPI AG

Link

https://www.mdpi.com/2411-5134/9/3/59/pdf

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