评论基于深度学习的 Omics 和临床病理数据生存分析

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY Inventions Pub Date : 2024-05-16 DOI:10.3390/inventions9030059
Julia Sidorova, Juan Jose Lozano
{"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)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Inventions
Inventions Engineering-Engineering (all)
CiteScore
4.80
自引率
11.80%
发文量
91
审稿时长
12 weeks
期刊最新文献
PI3SO: A Spectroscopic γ-Ray Scanner Table for Sort and Segregate Radwaste Analysis Aircraft Innovation Trends Enabling Advanced Air Mobility The Effect of Individual Hydrocarbons in the Composition of Diesel Fuel on the Effectiveness of Depressant Additives A Review of Available Solutions for Implementation of Small–Medium Combined Heat and Power (CHP) Systems Real-Time Precision in 3D Concrete Printing: Controlling Layer Morphology via Machine Vision and Learning Algorithms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1