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
  • [1] Machine learning and multi-omics in precision medicine for ME/CFS
    Huang, Katherine
    Lidbury, Brett A.
    Thomas, Natalie
    Gooley, Paul R.
    Armstrong, Christopher W.
    JOURNAL OF TRANSLATIONAL MEDICINE, 2025, 23 (01)
  • [2] Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning
    Wu, Jingyue
    Singleton, Stephanie S.
    Bhuiyan, Urnisha
    Krammer, Lori
    Mazumder, Raja
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2024, 10
  • [3] Machine learning for the analysis of multi-omics data
    Sun, Yanni
    METHODS, 2021, 189 : 1 - 2
  • [4] MACHINE LEARNING AND MATHEMATICAL MODELS FROM MULTI-OMICS DATA FOR PERSONALIZED MEDICINE
    Saez-Rodriguez, Julio
    TISSUE ENGINEERING PART A, 2022, 28 : S651 - S651
  • [5] InterTADs: integration of multi-omics data on topologically associated domains, application to chronic lymphocytic leukemia
    Tsagiopoulou, Maria
    Pechlivanis, Nikolaos
    Maniou, Maria Christina
    Psomopoulos, Fotis
    NAR GENOMICS AND BIOINFORMATICS, 2022, 4 (01)
  • [6] Methodology for Good Machine Learning with Multi-Omics Data
    Coroller, Thibaud
    Sahiner, Berkman
    Amatya, Anup
    Gossmann, Alexej
    Karagiannis, Konstantinos
    Moloney, Conor
    Samala, Ravi K.
    Santana-Quintero, Luis
    Solovieff, Nadia
    Wang, Craig
    Amiri-Kordestani, Laleh
    Cao, Qian
    Cha, Kenny H.
    Charlab, Rosane
    Cross, Frank H.
    Hu, Tingting
    Huang, Ruihao
    Kraft, Jeffrey
    Krusche, Peter
    Li, Yutong
    Li, Zheng
    Mazo, Ilya
    Paul, Rahul
    Schnakenberg, Susan
    Serra, Paolo
    Smith, Sean
    Song, Chi
    Su, Fei
    Tiwari, Mohit
    Vechery, Colin
    Xiong, Xin
    Zarate, Juan Pablo
    Zhu, Hao
    Chakravartty, Arunava
    Liu, Qi
    Ohlssen, David
    Petrick, Nicholas
    Schneider, Julie A.
    Walderhaug, Mark
    Zuber, Emmanuel
    CLINICAL PHARMACOLOGY & THERAPEUTICS, 2024, 115 (04) : 745 - 757
  • [7] Machine learning for multi-omics data integration in cancer
    Cai, Zhaoxiang
    Poulos, Rebecca C.
    Liu, Jia
    Zhong, Qing
    ISCIENCE, 2022, 25 (02)
  • [8] MULTI-OMICS TO EDGE INTO PRECISION MEDICINE FOR DIPG
    Tang, Nanyun
    Leskoske, Kristin
    Garcia-Mansfield, Krystine
    Sharma, Ritin
    Tolson, Hannah
    Pirrotte, Patrick
    Berens, Michael
    NEURO-ONCOLOGY, 2021, 23 : 40 - 40
  • [9] Dealing with dimensionality: the application of machine learning to multi-omics data
    Feldner-Busztin, Dylan
    Nisantzis, Panos Firbas
    Edmunds, Shelley Jane
    Boza, Gergely
    Racimo, Fernando
    Gopalakrishnan, Shyam
    Limborg, Morten Tonsberg
    Lahti, Leo
    de Polavieja, Gonzalo G.
    BIOINFORMATICS, 2023, 39 (02)
  • [10] Integration strategies of multi-omics data for machine learning analysis
    Picard M.
    Scott-Boyer M.-P.
    Bodein A.
    Périn O.
    Droit A.
    Computational and Structural Biotechnology Journal, 2021, 19 : 3735 - 3746