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

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2024-05-16 DOI:10.3390/inventions9030059

Julia Sidorova, Juan Jose Lozano

引用次数: 0

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).

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评论基于深度学习的 Omics 和临床病理数据生存分析

2017-2024年，基于深度生存分析的算法领域成果丰硕。我们寻找了以下三个问题的答案。(1) 临床数据分析是否已经有了新的 "黄金标准"？(2）DL 组件是否能显著提高性能？(3) 基于深度的生存是否存在非深度方法无法直接实现的实际优势？我们分析并比较了针对两种输入类型设计的具有影响力的选定算法：临床病理学数据（一小部分数字、二元和分类数据）和 omics 数据（数字和极高维数据，具有明显的 p >> n 复杂性）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464

期刊介绍： ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.