Computational Tools in Clinical Oncology

Pp: 188-200 (13)

* (Excluding Mailing and Handling)

Abstract

With the advancement in the sequencing technology, diagnosis, and new treatment methods in the field of oncology arises some new challenges to analyze such big data generated as a result. These challenges also lead to alternative approaches by which it can be solved. One such approach is the use of computational technology in the field to analyze, predict and respond with high accuracy to the problem. With the use of many web-based and offline computational tools, it now becomes easier to analyze and predict the result with high accuracy that could not be possible by using the human brain only. This book chapter summarizes some of these such tools related to analyzing multi-omic cancer molecular data, biomarker discovery, digital pathology tools for diagnosis and image deconvolution tools in the field of clinical oncology.

Keywords: Computational Tools, Cancer, DeMixt, FUNSEQ2, HistoQC, PROMO, Qupath, SURVNET, TANRIC, Tumor Map, UALCAN, Xena.