ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

Irina KalatskayaQuang M TrinhMelanie SpearsJohn D McPhersonJohn M S BartlettLincoln Stein

Published in: Genome medicine (2017)

In this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN .

Keyphrases

gene expression
copy number
papillary thyroid
high resolution
primary care
machine learning
healthcare
deep learning
squamous cell
mass spectrometry
neural network