RESEARCH

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Overview
3D Chromosome and Genome Structure Modeling using Machine Learning and Visualization
Hi-C data Enhancement and Forecasting using Deep Learning
Algorithms for the TADs and Chromatin loops detection using Machine Learning
Splice Site Prediction using Deep Learning

Hi-C Data Enhancement and Forecasting using Deep Learning

Fall 2019 – present

Keywords: Hi-C; Deep Learning; Bioinformatics; Resolution Enhancement; 3D Genome; Super-Resolution

Description:

High throughput chromosome conformation capture (Hi-C) contact matrices are used to predict three-dimensional (3D) chromatin structures in eukaryotic cells. High resolution Hi-C data are less available than low resolution Hi-C data due to sequencing costs, but provide greater insight into the intricate details of 3D chromatin structures such as enhancer-promoter interactions and sub-domains. To provide a cost effective solution to high resolution Hi-C data collection, deep learning models are used to predict high resolution Hi-C matrices from existing low resolution matrices across multiple cell types.

Publications:

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Codes:

All our algorithms are made public, open-source, and freely accessible to all through our GitHub repository

OFFICE NO:
Discovery Park – F209
EMAIL:
oluwatosin.oluwadare [at] unt [dot] edu

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LAB ADDRESS: UNT Discovery Park – Center for Computational Life Sciences, F296