Welcome to Dr. Oluwadare's Bioinformatics Lab

This Research group focuses on developing efficient algorithms, computational methods, and relevant tools and softwares algorithms to solve interesting and important biological problems. We use machine learning, deep learning, and artificial intelligence (AI) methods to analyze big biomedical data to help address complex biological questions.

Currently, here are our major projects:

Bioinformatics Lab on GitHub

Research Sponsors:

NSF Logo NSF Logo             NIH Logo

Open Positions
Graduate Research Assistant(GRA) Positions:
GRA Positions are available for Self-motivated and enthusiastic PhD students interested in bioinformatics, machine learning and data mining. GRA Positions details.pdf
Undergraduate Research Opportunities:
Research Opportunities are available for highly motivated and hardworking undergraduate students who wish to work on exciting research problems. Undergraduate Positions details.pdf
UCCS Research Experience for Undergraduates (REU) Opportunities in Bioinformatics:
I am considering highly motivated and hardworking undergraduate students for REU position for Deep Learning in Natural Language Processing, Bioinformatics, at UCCS. You can apply immediately if you meet the qualifications. See Application Details here: REU in Bioinformatics Research
UCCS Summer Undergraduate Research Academy: See the Application Instruction here: Undergraduate Research Academy . I am open to being a faculty mentor for interested students.

Our Goal

The Bioinformatics Lab is directed by Dr Oluwatosin Oluwadare and is located the Department of Computer Science at the University of Colorado, Colorado Springs
Softwares Quick link
CNNSplice: Robust Models for Splice Site Prediction Using Convolutional Neural Networks
ParticleChromo3D+: A Web Server for ParticleChromo3D Algorithm for 3D Chromosome Structure Reconstruction
TADMaster: A Comprehensive Web-based Tool for the Analysis of Topologically Associated Domains
EnsembleSplice: Ensemble Deep Learning Model for Splice Site Prediction
HiCARN: Resolution Enhancement of Hi-C Data Using Cascading Residual Networks
HiC-GNN: A Generalizable Model for 3D Chromosome Reconstruction Using Graph Convolutional Neural Networks
DeepSplicer: An Improved Method of Splice Sites Prediction using Deep Learning.
ChromeBat: A Bio-Inspired Approach to Genome Reconstruction.
CBCR: A Curriculum Based Strategy For Chromosome Reconstruction
ParticleChromo3D: 3D chromosome structure reconstruction using Particle Swarm Optimization Algorithm.
GSDB: centralized collection of chromosome and genome structures.
Presentation of Our Recent Work