Deep Learning for Genomics: A Concise Overview

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.

References in corpus (12)

• An Overview of Multi-Task Learning in Deep Neural Networks
• dna2vec: Consistent vector representations of variable-length k-mers
• Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks
• On the Origin of Deep Learning
• Deep Motif: Visualizing Genomic Sequence Classifications
• Distributed Representations for Biological Sequence Analysis
• Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition
• Dr.VAE: Drug Response Variational Autoencoder
• Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning
• Implicit Causal Models for Genome-wide Association Studies
• Evaluating deep variational autoencoders trained on pan-cancer gene expression
• MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

Cited by in corpus (7)

• A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate
• Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates
• Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning
• Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics
• GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning
• A Neurochaos Learning Architecture for Genome Classification
• Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference