Mehwish Gill, Muhammad Kabir, Saeed Ahmed, Muhammad Asif Subhani, Maqsood Hayat
{"title":"A Comparative Review and Analysis of Computational Predictors for\nIdentification of Enhancer and their Strength","authors":"Mehwish Gill, Muhammad Kabir, Saeed Ahmed, Muhammad Asif Subhani, Maqsood Hayat","doi":"10.2174/0115748936285942240513064919","DOIUrl":null,"url":null,"abstract":"\n\nEnhancers are the short functional regions (50–1500bp) in the genome, which play an\neffective character in activating gene-transcription in the presence of transcription-factors (TFs).\nMany human diseases, such as cancer and inflammatory bowel disease, are correlated with the enhancers’\ngenetic variations. The precise recognition of the enhancers provides useful insights for\nunderstanding the pathogenesis of human diseases and their treatments. High-throughput experiments\nare considered essential tools for characterizing enhancers; however, these methods are laborious,\ncostly and time-consuming. Computational methods are considered alternative solutions for\naccurate and rapid identification of the enhancers. Over the past years, numerous computational\npredictors have been devised for predicting enhancers and their strength. A comprehensive review\nand thorough assessment are indispensable to systematically compare sequence-based enhancer’s\nbioinformatics tools on their performance. Giving the increasing interest in this domain, we conducted\na large-scale analysis and assessment of the state-of-the-art enhancer predictors to evaluate\ntheir scalability and generalization power. Additionally, we classified the existing approaches into\nthree main groups: conventional machine-learning, ensemble and deep learning-based approaches.\nFurthermore, the study has focused on exploring the important factors that are crucial for developing\nprecise and reliable predictors such as designing trusted benchmark/independent datasets, feature\nrepresentation schemes, feature selection methods, classification strategies, evaluation metrics\nand webservers. Finally, the insights from this review are expected to provide important guidelines\nto the research community and pharmaceutical companies in general and high-throughput tools for\nthe detection and characterization of enhancers in particular.\n","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"184 1‐2","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.2174/0115748936285942240513064919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Enhancers are the short functional regions (50–1500bp) in the genome, which play an
effective character in activating gene-transcription in the presence of transcription-factors (TFs).
Many human diseases, such as cancer and inflammatory bowel disease, are correlated with the enhancers’
genetic variations. The precise recognition of the enhancers provides useful insights for
understanding the pathogenesis of human diseases and their treatments. High-throughput experiments
are considered essential tools for characterizing enhancers; however, these methods are laborious,
costly and time-consuming. Computational methods are considered alternative solutions for
accurate and rapid identification of the enhancers. Over the past years, numerous computational
predictors have been devised for predicting enhancers and their strength. A comprehensive review
and thorough assessment are indispensable to systematically compare sequence-based enhancer’s
bioinformatics tools on their performance. Giving the increasing interest in this domain, we conducted
a large-scale analysis and assessment of the state-of-the-art enhancer predictors to evaluate
their scalability and generalization power. Additionally, we classified the existing approaches into
three main groups: conventional machine-learning, ensemble and deep learning-based approaches.
Furthermore, the study has focused on exploring the important factors that are crucial for developing
precise and reliable predictors such as designing trusted benchmark/independent datasets, feature
representation schemes, feature selection methods, classification strategies, evaluation metrics
and webservers. Finally, the insights from this review are expected to provide important guidelines
to the research community and pharmaceutical companies in general and high-throughput tools for
the detection and characterization of enhancers in particular.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.