Deep Learning for Genomics: A Concise Overview

TL;DR

This paper reviews how deep learning models are applied to genomic research, highlighting their strengths, challenges, and future opportunities in handling big genomic data.

Contribution

It provides a concise overview of deep learning architectures tailored for genomics and discusses practical considerations and future directions.

Findings

Different deep learning models are suited for specific genomic tasks.

Deep learning faces unique challenges in genomics due to data complexity.

Future opportunities include improved models and integration with genomic knowledge.

Abstract

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.