Apply Distributed CNN on Genomics to accelerate Transcription-Factor TAL1 Motif Prediction

TL;DR

This paper demonstrates that applying distributed CNNs with GPU and TPU accelerators significantly reduces training time and improves accuracy in genomics-based transcription factor motif prediction, specifically for TAL1.

Contribution

It introduces a distributed deep learning approach using CNNs and accelerators to speed up and enhance transcription factor motif prediction in genomics.

Findings

Training time decreased with distributed CNNs

Achieved 95% accuracy in TAL1 motif prediction

Effective use of GPU and TPU accelerators

Abstract

Big Data works perfectly along with Deep learning to extract knowledge from a huge amount of data. However, this processing could take a lot of training time. Genomics is a Big Data science with high dimensionality. It relies on deep learning to solve complicated problems in certain diseases like cancer by using different DNA information such as the transcription factor. TAL1 is a transcription factor that is essential for the development of hematopoiesis and of the vascular system. In this paper, we highlight the potential of deep learning in the field of genomics and its challenges such as the training time that takes hours, weeks, and in some cases months. Therefore, we propose to apply a distributed deep learning implementation based on Convolutional Neural Networks (CNN) that showed good results in decreasing the training time and enhancing the accuracy performance with 95% by using multiple GPU and TPU as accelerators. We proved the efficiency of using a distributed strategy based on data-parallelism in predicting the transcription-factor TAL1 motif faster.