组学
精密医学
数据集成
背景(考古学)
计算机科学
人工智能
机器学习
生物标志物发现
数据科学
代谢组学
蛋白质组学
计算生物学
生物
生物信息学
数据挖掘
遗传学
古生物学
生物化学
基因
作者
Debabrata Acharya,Anirban Mukhopadhyay
出处
期刊:Briefings in Functional Genomics
[Oxford University Press]
日期:2024-04-10
被引量:1
摘要
Abstract Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in
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