The goal of the Ahmed Lab is to build intelligent health systems that systematically incorporate clinical, multi-omics, and phenotypic data into healthcare for providing personalized treatment and better life. We are a computational lab, driven towards the development of bioinformatics tools and methods for multi-omics and clinical data management, processing, integration, annotation, interpretation, and Artificial Intelligence/Machine Learning (AI/ML) ready data generation and sharing. Our focus is on implementing AI/M approaches to the whole genome and transcriptome data for the identification of patterns revealing predictive biomarkers and risk factors to support earlier diagnosis of patients with complex traits, including Cardiovascular, COVID-19, and Alzheimer’s diseases.
We have already designed, developed, and practiced many bioinformatics tools, genomics pipelines, AI/ML algorithms, annotation databases, mobile health platforms, high-performance computing (HPC) based frameworks for multivariate clinical and multi-omics data analysis and dissemination. Furthermore, we have implemented dynamic HIPAA-compliant infrastructures to support various scientific studies with efficient patient’s recruitment, collected sample’s data management, and integration with electronic healthcare records (EPIC, NextGen). Our research plan is underpinned with skills and resource development to build expertise in sequence-based genomic analysis, clinical variant interpretation, and evidence-based diagnostic and prediction model development and validation.
Our most recent innovations include GVViZ, PAS, MAV-clic, and Hygieia. GVViZ is an interactive and user-friendly application, which supports gene-disease annotation, expression analysis, visualization, and automated transcriptomic profile generation of patients. PAS is an iOS application that offers access to an annotated human gene-variants-disease database, including over 200,000 gene-disease combinations for translational research. MAV-clic is a biomedical informatics application capable of efficienlty extracting and analyzing electronic health records (EHR) of consented patients from the health systems including EPIC and NextGen. Hygieia is an AI/ML pipeline integrating healthcare and genomics data to investigate genes associated with targeted disorders and predict disease.
Our recent publications have been valued globally, invited for talks, and recognized among featured, high-impact research, top and most cited articles. Due to the good reputation and global acceptance, our bioinformatics applications have collectively over 5,000 users worldwide. Furthermore, we have been invited for several talks and workshops at many reputed international events and conferences.
Mission & Vision
To improve the quality and transition of healthcare, robust data management platforms are necessary to analyze heterogeneous genomics and clinical data of high volume, velocity, variety and veracity. Healthcare data includes information about patient life style, medical history, visits to the practice, lab and imaging test, diagnoses, medications, surgical procedures, consulted providers, claims and genomics profile. Adequate and analytic access to the healthcare and genomics data has potential to revolutionize the field of medicine by developing better understanding of biological mechanisms and modelling complex biological interactions by integrating and analyzing knowledge in a holistic manner. To effectively meet the goals of implementing system for precision medicine, significant efforts are required from the experts in various disciplines (e.g. clinics, bench/wet labs, core/genome technologies, bioinformatics etc.), located within one or multiple organizational units. One of the major challenges is to establish an efficient and secure workflow that can connect all units to streamline transparent data flow, interoperability, cleansing, structuring, standardization, controlled accessibility, processing, quality inspection, processing, analysis, visualization, reporting and sharing.
This precision medicine project is by Ahmed Lab