Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

被引：14

作者：

Lu, Shasha ^{[1
]}

Yang, Jianyu ^{[1
]}

Gu, Yu ^{[1
]}

He, Dongyuan ^{[1
]}

Wu, Haocheng ^{[1
]}

Sun, Wei ^{[2
]}

Xu, Dong ^{[3
]}

Li, Changming ^{[1
]}

Guo, Chunxian ^{[1
]}

机构：

[1] Suzhou Univ Sci & Technol, Sch Mat Sci & Engn, Suzhou 215011, Peoples R China

[2] Hainan Normal Univ, Coll Chem & Chem Engn, Haikou 571158, Peoples R China

[3] Chinese Acad Sci, Zhejiang Canc Hosp, Hangzhou Inst Med HIM, Dept Diagnost Ultrasound Imaging & Intervent Thera, Hangzhou 310022, Peoples R China

来源：

ACS SENSORS | 2024年 / 9卷 / 03期

基金：

中国国家自然科学基金;

关键词：

biomarker; big data; machine learning; data mining; bioinformatics; artificial intelligence; molecular computing; modular workflow; diseasediagnosis sensor; BREAST-CANCER; ARTIFICIAL-INTELLIGENCE; PROSTATE-CANCER; T-SNE; DISCRIMINANT-ANALYSIS; MASS-SPECTROMETRY; RNA-SEQ; CLASSIFICATION; IDENTIFICATION; TRANSLATION;

D O I：

10.1021/acssensors.3c02670

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

引用

页码：1134 / 1148

页数：15

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