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Prioritizing predicted cis-regulatory elements for co-expressed gene sets based on Lasso regression models
Hong Hu1, Damian Roqueiro, Yang Dai
1Department of Bioengineering (M/C 063), University of Illinois at Chicago, 851 S Morgan St, SEO 218, Chicago, IL 60607, USA. hhu4@uic.edu
Prioritizing transcription factors for gene regulation studies is challenging. A new Lasso regression model effectively identifies key factors using gene expression data from mouse wound healing.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Predicting cis-regulatory elements yields many potential transcription factor binding sites.
- Prioritizing these factors for functional studies is a significant challenge in gene regulation research.
Purpose of the Study:
- To introduce and evaluate a novel approach using Lasso regression models for prioritizing transcription factors.
- To assess the efficacy of the Lasso model in analyzing gene expression data from mouse wound healing.
Main Methods:
- Utilized Lasso regression models for computational prediction and prioritization of transcription factors.
- Analyzed time-course microarray data from mouse skin and mucosal wound healing stages.
Main Results:
- The Lasso regression model demonstrated effectiveness in prioritizing transcription factors from large datasets.
- Successfully applied the model to identify key regulatory factors during the complex process of wound healing.
Conclusions:
- Lasso regression offers a robust method for prioritizing transcription factors in regulatory genomics.
- This approach aids in focusing experimental efforts on the most relevant transcription factors for functional validation.
Related Concept Videos
Cis-regulatory Sequences
Cis-regulatory Sequences
Cooperative Binding of Transcription Regulators
Cooperative Binding of Transcription Regulators
Co-activators and Co-repressors
Co-activators and Co-repressors

