{"title":"Review: Deep Learning-Based Survival Analysis of Omics and Clinicopathological Data","authors":"Julia Sidorova, Juan Jose Lozano","doi":"10.3390/inventions9030059","DOIUrl":null,"url":null,"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).","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"8 6","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/inventions9030059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
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).
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.
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