박종은 (제주대학교)

한빛사논문

조회76

제주대학교

Biotechnol. Adv., Sep 07 2024, S0734-9750(24)00141-1 | https://doi.org/10.1016/j.biotechadv.2024.108447 Advance computational tools for multiomics data learning

Sheikh Mansoor^a, Saira Hamid^b, Thai Thanh Tuan ^b, Jong Eun Park ^c, Yong Suk Chung ^a

^aDepartment of Plant Resources and Environment, Jeju National University, 63243, Republic of Korea

^bWatson Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Awantipora, Pulwama, J&K, India

^cDepartment of Animal Biotechnology, College of Applied Life Science, Jeju National University, Jeju, Jeju-do, Republic of Korea

Corresponding authors : Jong Eun Park, Yong Suk Chung

Abstract

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial roles in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

논문정보

형식Review Paper
게재일2024년 09월 (BRIC 등록일 2024-09-11)
연구진국내 연구진
분야 생명과학 > 생명과학 기타

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