Deep learning identifies molecular patterns of cancer

A new deep-learning algorithm can quickly and accurately analyze several types of genomic data from colorectal tumors for more accurate classification, which could help improve diagnosis and related treatment options, according to new research published in the journal Life Science Alliance. Colorectal tumors are extremely varied in how they develop, require different drugs and have very different survival rates. Often, they are classified into subtypes based on analysis of gene expression levels. "Disease is much more complex than just one gene," said Altuna Akalin, bioinformatics scientist who leads the Bioinformatics Platform research group at MDC's Berlin Institute of Medical Systems Biology (BIMSB). "To appreciate the complexity, we have to use some kind of machine learning to really make use of all the data."