Design of Machine Learning Models for the Prediction of Transcription Factor Binding Regions in Bacterial DNA
Title
Design of Machine Learning Models for the Prediction of Transcription Factor Binding Regions in Bacterial DNADate
2021Citation
Alvarez-Gonzalez, S.; Erill, I. Design of Machine Learning Models for the Prediction of Transcription Factor Binding Regions in Bacterial DNA. Eng. Proc. 2021, 7, 59. https://doi.org/10.3390/engproc2021007059
Abstract
[Abstract] Transcription Factors (TFs) are proteins that regulate the expression of genes by binding to their promoter regions. There is great interest in understanding in which regions TFs will bind to the DNA sequence of an organism and the possible genetic implications that this entails. Occasionally, the sequence patterns (motifs) that a TF binds are not well defined. In this work, machine learning (ML) models were applied to TF binding data from ChIP-seq experiments. The objective was to detect patterns in TF binding regions that involved structural (DNAShapeR) and compositional (kmers) characteristics of the DNA sequence. After the application of random forest and Glmnet ML techniques with both internal and external validation, it was observed that two types of generated descriptors (HelT and tetramers) were significantly better than the others in terms of prediction, achieving values of more than 90%.
Keywords
Transcription factor
Machine learning
Protein binding
Machine learning
Protein binding
Description
Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.
Editor version
Rights
Atribución 4.0 Internacional