Application of Deep Learning to Biomedical Informatics

Abstract

Deep learning has become a popular machine learning technology since the first fast learning algorithm for training a deep belief neural net in 2006 [1]. There are over 2,800 deep learning publications indexed by Scopus only in the year of 2015. The origin definition of deep learning usually means the multi-layer artificial neural nets. Now-a-days, the definition has been generalized that any combination of computation models that “are composed of multiple processing layers to learn representations of data with multiple levels of abstraction” is called a deep learning model [2]. Deep learning has been successfully applied to many fields, such as image recognition [3], speech recognition [4], and machine translation [5], and embedded into industrial systems, like AlphaGo developed by Google DeepMind. The success of deep learning has brought new insights into the medical domain where there are large quantities of data available. For example, there are a large number of genes across the whole human genome and whole-genome gene expression profiling is still very expensive in typical academic labs to by considering a large number of conditions, such as genetic perturbations [6]. Taking advantage of the availability of “big data” in bioinformatics, deep learning has been widely utilized in gene expression regulation [7], protein structure prediction [8], drug discovery [9] and so forth.