4.5 Review

Computational resources for identification of cancer biomarkers from omics data

期刊

BRIEFINGS IN FUNCTIONAL GENOMICS
卷 20, 期 4, 页码 213-222

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bfgp/elab021

关键词

omics data; cancer biomarker; web server; computational resource; diagnosis; prognosis

资金

  1. J. C. Bose National Fellowship fromDepartment of Science & Technology (DST), India [SRP076]
  2. UGC, India
  3. Council of Scientific and Industrial Research (CSIR), India

向作者/读者索取更多资源

This review provides a comprehensive summary of various omics data related to cancer and the computational resources and tools available to assist researchers and clinicians in effectively managing cancer and improving patient outcomes.
Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.

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