Prediction of Genetic Biomarkers from RNA-Seq Dataset of Colon Cancer

被引：0

作者：

Adeyemi, Tijesunimi ^{[1
]}

Ezekiel, Deborah ^{[1
]}

Diaz, Sergio ^{[2
]}

Sabb, Felix ^{[3
]}

Abdul, Abdullah ^{[4
]}

Nembhard, Fitzroy ^{[5
]}

Paudel, Roshan ^{[1
]}

机构：

[1] Morgan State Univ, Dept Comp Sci, Baltimore, MD 21239 USA

[2] Univ Maryland Baltimore Cty, Dept Phys, Baltimore, MD 21228 USA

[3] Baltimore Cty Publ Sch, Baltimore, MD USA

[4] Morgan State Univ, Dept Comp Sci, Baltimore, MD 21239 USA

[5] Florida Inst Technol, L3Harris Inst Assured Informat, Melbourne, FL 32901 USA

来源：

2023 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE, CSCI 2023 | 2023年

关键词：

Colorectal cancer; machine learning; gene; biomarkers; HEREDITARY; MUTATION; HMLH1; RISK;

D O I：

10.1109/CSCI62032.2023.00226

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study focuses on the critical role of genetic biomarkers in diagnosing, treating, and preventing the development of colon cancer. The identification of these biomarkers enhances the precision of colon cancer diagnosis and classification. Detecting specific genetic mutations makes distinctions among various colon cancer types feasible. Analyzing upregulated and down-regulated genes aids in identifying individuals at higher risk of colon cancer development. Employing a machine learning approach, this research predicts genetic biomarkers linked to colon cancer, revealing LASP1 as an overexpressed and ICA1 as an under-expressed gene. Additionally, the study predicts seven genetic biomarkers associated with colon cancer, including WNT16, MAD1L1, TMEM176A, M6PR, CYP26B1, ICA1, and LASP1

引用

页码：1378 / 1385

页数：8

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