A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data.

ABSTRACT: Cancer classification is a topic of major interest in medicine since it allows accurate and efficient diagnosis and facilitates a successful outcome in medical treatments. Previous studies have classified human tumors using a large-scale RNA profiling and supervised Machine Learning (ML) algorithms to construct a molecular-based classification of carcinoma cells from breast, bladder, adenocarcinoma, colorectal, gastro esophagus, kidney, liver, lung, ovarian, pancreas, and prostate tumors. These datasets are collectively known as the 11_tumor database, although this database has been used in several works in the ML field, no comparative studies of different algorithms can be found in the literature. On the other hand, advances in both hardware and software technologies have fostered conside

SUBMITTER: Tabares-Soto R 

PROVIDER: S-EPMC7924492 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature