Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations.

ABSTRACT:

Background

Machine learning (ML) represents a powerful tool to capture relationships between molecular alterations and cancer types and to extract biological information. Here, we developed a plain ML model aimed at distinguishing cancer types based on genetic lesions, providing an additional tool to improve cancer diagnosis, particularly for tumors of unknown origin.

Methods

TCGA data from 9,927 samples spanning 32 different cancer types were downloaded from cBioportal. A vector space model type data transformation technique was designed to build consistently homogeneous new datasets containing, as predictive features, calls for somatic point mutations and copy number variations at chromosome arm-level, thus allowing the use of the XGBoost classifier models. Considering the

SUBMITTER: Zelli V 

PROVIDER: S-EPMC10664515 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature