Deep learning in non coding variant (a brief overview)

Abstract

<span>The 21st centuries were deemed to be the era of big data. Data driven research had become a necessity. This hold true not only in the business world, yet also in the field of biomedical world. From a few years of biological data extraction and derivation. With the advancement of Next Generation Sequencing, genomics data had grown to become an ambiguous giant which could not keep up with the pace of its advancement in it analysis counter parts. This results in a large amount of unanalysed genomic data. These genomic data consist not only plain information, researcher had discovered the potential of most gene called the non-coding variant and still failing in identifying their function. With the growth in volume of data, there is also a growth of hardware or technologies. With current technologies, we were able to implement a more complex and sophisticated algorithm in analysis these genomics data. The domain of deep learning had become a major interest of researcher as it was proven to have achieve a significant success in deriving insight from various field. This paper aims to review the current trend of non-coding variant analysis using deep learning approach.</span>

A framework for predicting lncRNAs expression in human dendritic cells in response to M. tuberculosis infection

“…In predicting non coding RNAs, it had been proven that the prediction performance of tools that apply deep learning methods exceeded other traditional machine learning methods in terms of identification reliability, ease of use and ability to utilize features not incorporated in the current knowledge [33], [34].…”