Recently Emerged Genome Wide Computational Enhancer Target Prediction Tools: A Brief Survey

Authors: Lim LWK*

DOI: 10.23880/izab-16000440

Volume 6, Issue 1

Abstract

Enhancers are non-coding genomic regulatory elements capable of elevating gene transcription in various biological as well as developmental stages in the host organism. Discovered since 1981, the enhancers play major roles in genetic disease onset and development, orchestrating gene regulation patterns even across the same species via the sequence variations. To date, predicting enhancers and their targets remain a daunting task as universal enhancer markers are yet to be discovered. Computational enhancer target prediction involves three major approaches: supervised, unsupervised and semi-supervised machine learning methods which work on enhancer target features such as enhancer-promoter distance, closest promoter, co-conservation and correlation of molecular signals. In this review, we introduced some recently emerged enhancer target prediction tools as well as their modus operandi, in hope that we can provide future directions towards the development of a more robust tool to aid in the advancement of enhancer targeted treatment researches.

Keywords: Enhancer Target Prediction; Supervised Learning; Unsupervised Learning; Semi-Supervised Learning

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