Personalized disease signatures through information-theoretic compaction of big cancer data.

ABSTRACT: Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study

SUBMITTER: Vasudevan S 

PROVIDER: S-EPMC6065026 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature