{"title":"评论基于深度学习的 Omics 和临床病理数据生存分析","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":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":14564,\"journal\":{\"name\":\"Inventions\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Inventions\",\"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\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inventions","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":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
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）。
Review: Deep Learning-Based Survival Analysis of Omics and Clinicopathological Data
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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