The Saeed Lab develops machine-learning models, combined with high-performance computing, and data science approaches, to study the functional genomics and organization of the human brain.

Our work focuses on understanding the stochastic difference between identified peptides from high-throughput mass spectrometry data for applications related to human health, disease, and environment. In addition, our work focuses on understanding brain function in the context of prediction, diagnosis and characterization of biomarkers specific to disorders such as epilepsy, ADHD, Autism, and Alzheimer’s. Improved computational methods may impact detection of novel biomarkers and non-abundant proteins, and better diagnosis/prediction outcomes for the patients.

To achieve these goals, we embrace open science principles and adopt and develop best practices to promote reproducible computational results. If you would like to know more about specific projects, you are welcome to visit us on GitHub and Software pages.

MLSPred-Bench: Reference EEG Benchmark for Prediction of Epileptic Seizures Reference EEG Benchmark for Prediction of Epileptic Seizures

Characterization and diagnosis of Autism Spectrum Machine-learning models that can classify between autistic and neurotypical brains	ML Ecosystem for Mass Spectrometry Data Development of interconnected set of open-source machine-learning tools for mass spectrometry based omics
Predicting Epileptic Seizures Advance machine-learning models for prediction of Seizures using EEG data	HPC Engine for Mass Spectrometry based Omics Data Design and development of high-performance computing algorithms for large-scale MS omics data using hetregenous architectures

Compressive and reductive analysis of genomic and proteomics data Development of methods that can operate on compressed big data