{"title":"Advance computational tools for multiomics data learning","authors":"Sheikh Mansoor , Saira Hamid , Thai Thanh Tuan , Jong-Eun Park , Yong Suk Chung","doi":"10.1016/j.biotechadv.2024.108447","DOIUrl":null,"url":null,"abstract":"<div><p>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 role 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.</p></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"77 ","pages":"Article 108447"},"PeriodicalIF":12.1000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biotechnology advances","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0734975024001411","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
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 role 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.
期刊介绍:
Biotechnology Advances is a comprehensive review journal that covers all aspects of the multidisciplinary field of biotechnology. The journal focuses on biotechnology principles and their applications in various industries, agriculture, medicine, environmental concerns, and regulatory issues. It publishes authoritative articles that highlight current developments and future trends in the field of biotechnology. The journal invites submissions of manuscripts that are relevant and appropriate. It targets a wide audience, including scientists, engineers, students, instructors, researchers, practitioners, managers, governments, and other stakeholders in the field. Additionally, special issues are published based on selected presentations from recent relevant conferences in collaboration with the organizations hosting those conferences.