Advance computational tools for multiomics data learning

Mansoor S., Hamid S., Tuan T. T., Park J., Chung Y. S.

BIOTECHNOLOGY ADVANCES, cilt.77, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 77
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.biotechadv.2024.108447
  • Dergi Adı: BIOTECHNOLOGY ADVANCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Communication Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, INSPEC, MEDLINE, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Hayır

Özet

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, fusionbased 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.