Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients.

ABSTRACT: In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such dataset describing patients with bladder cancer, mostly non-muscle-invasive (NMIBC), and propose a novel approach to feature selection. This approach returns high-quality features for prediction and yet allows interpretability as well as a certain level of insight into the analyzed data. As a result

SUBMITTER: Piliszek R 

PROVIDER: S-EPMC9266725 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature