Deep learning for computational biology. Issue 7 (July 2016)

• Record Type: Journal Article
• Authors: Angermueller, Christof; Pärnamaa, Tanel; Parts, Leopold; Stegle, Oliver
• Is Part Of: Molecular systems biology. Volume 12:Issue 7(2016:Jul.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-07
• Subjects: cellular imaging -- computational biology -- deep learning -- machine learning -- regulatory genomics; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.20156651
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology. Abstract : Deep learning, a class of modern machine learning methods, has become a go‐to approach for analysing large‐scale high‐dimensional data. This review discusses its applications in biology, focusing on regulatory genomics and cellular imaging, and gives guidelines for practitioners.