Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine?

被引:5
|
作者
Tsagiopoulou, Maria [1 ]
Gut, Ivo G. [1 ,2 ]
机构
[1] Ctr Nacl Anal Genom CNAG, Barcelona, Spain
[2] Univ Barcelona UB, Barcelona, Spain
关键词
machine Learning; omics; multi-omics analysis; precision medicine; chronic lymphocytic leukemia (CLL); bioinformatics; NGS -next generation sequencing; SUBGROUPS; FORM;
D O I
10.3389/fgene.2023.1304661
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.
引用
收藏
页数:7
相关论文
共 50 条
  • [21] The Need for Multi-Omics Biomarker Signatures in Precision Medicine
    Olivier, Michael
    Asmis, Reto
    Hawkins, Gregory A.
    Howard, Timothy D.
    Cox, Laura A.
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2019, 20 (19)
  • [22] Multi-omics to edge into precision medicine for DIPG.
    Weller, Sandra
    Schaefer, Benjamin
    Beigl, Tobias
    Boepple, Kathrin
    Aulitzky, Walter E.
    Kopp, Hans-Georg
    Essmann, Frank
    CANCER RESEARCH, 2021, 81 (13)
  • [23] Using machine learning approaches for multi-omics data analysis: A review
    Reel, Parminder S.
    Reel, Smarti
    Pearson, Ewan
    Trucco, Emanuele
    Jefferson, Emily
    BIOTECHNOLOGY ADVANCES, 2021, 49
  • [24] Machine Learning for multi-omics data integration and variant pathogenicity estimation
    Li, Shuang
    van der Velde, K. Joeri
    Swertz, Morris A.
    2018 IEEE 14TH INTERNATIONAL CONFERENCE ON E-SCIENCE (E-SCIENCE 2018), 2018, : 301 - 301
  • [25] Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
    Arjmand, Babak
    Hamidpour, Shayesteh Kokabi
    Tayanloo-Beik, Akram
    Goodarzi, Parisa
    Aghayan, Hamid Reza
    Adibi, Hossein
    Larijani, Bagher
    FRONTIERS IN GENETICS, 2022, 13
  • [26] Precision Medicine Management of Chronic Lymphocytic Leukemia
    Moia, Riccardo
    Patriarca, Andrea
    Schipani, Mattia
    Ferri, Valentina
    Favini, Chiara
    Sagiraju, Sruthi
    Al Essa, Wael
    Gaidano, Gianluca
    CANCERS, 2020, 12 (03)
  • [27] A machine learning tool for spatial multi-omics
    Ang, Kok Siong
    Chen, Jinmiao
    NATURE METHODS, 2024, 21 (09) : 1593 - 1594
  • [28] MOBCdb: a comprehensive database integrating multi-omics data on breast cancer for precision medicine
    Xie, Bingbing
    Yuan, Zifeng
    Yang, Yadong
    Sun, Zhidan
    Zhou, Shuigeng
    Fang, Xiangdong
    BREAST CANCER RESEARCH AND TREATMENT, 2018, 169 (03) : 625 - 632
  • [29] MOBCdb: a comprehensive database integrating multi-omics data on breast cancer for precision medicine
    Bingbing Xie
    Zifeng Yuan
    Yadong Yang
    Zhidan Sun
    Shuigeng Zhou
    Xiangdong Fang
    Breast Cancer Research and Treatment, 2018, 169 : 625 - 632
  • [30] HFIP: an integrated multi-omics data and knowledge platform for the precision medicine of heart failure
    Wu, Jing
    Zhao, Min
    Li, Tao
    Sun, Jinxiu
    Chen, Qi
    Yin, Chengliang
    Jia, Zhilong
    Zhao, Chenghui
    Lin, Gui
    Ni, Yuan
    Xie, Guotong
    Shi, Jinlong
    He, Kunlun
    DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION, 2021,