Research at the Intersection of Computing and Biomedical Science
AI | Informatics | Data Science | Genetics | Clinical Decision Support | Public HealthAbout the Masino Lab
The Masino Lab is part of the School of Computing and the Center for Human Genetics at Clemson University. The overarching goal of the lab is to develop and apply computational methods for biomedical science to improve health outcomes and advance scientific knowledge. In pursuit of this goal, we focus on creating innovative artificial intelligence (AI), bioinformatics, biomedical informatics, and data science methods that can leverage multiple data modalities including multiomic, image, text, physiological waveform, and structured electronic health record (EHR) data. We seek to apply these methods to elucidate factors characterizing and contributing to human disease; identify latent structure within human diseases; and develop and evaluate innovative AI based decision support concepts. Our research is highly interdisciplinary and collaborative. We work with a diverse group of researchers, clinicians, policy makers, and students with diverse backgrounds in computer science, epidemiology, informatics, genetics, and medicine. We are always looking for new collaborations and students to join our team. We are committed to open science and the principles of reproducible research. We strive to make our code, data, and results publicly available whenever possible.News
COBRE in Human Genetics Research Project AwardThe Masino Lab is excited to announce that we have been awarded Research Project funding from the Center of Biomedical Research Excellence (COBRE) in Human Genetics. This award will support our research on developing and applying deep learning methods to accelerate discovery and diagnosis of rare genetic diseases.
Active Research
Discover more about how the Masino Lab works at the intersection of artificial intelligence and biomedical science.AI for Rare Genetic Disorder Research and Clinical Diagnosis
In collaboration with the Center of Biomedical Research Excellence (COBRE) in Human Genetics, we are developing and applying deep learning methods to further our understanding of rare genetic disorders and accelerate their diagnosis. This multi-year project seeks to alleviate the burden of rare genetic diseases through development of novel AI capabilities that will facilitate mechanistic discoveries, expedite diagnoses, and inform treatment through the following aims:- Develop AI methods for shared phenotype discovery within a population given structured and unstructured observational data in the absence of a disease specification.
- Develop a framework for integrating biological knowledge into multiomic deep learning models with application to tissue specific aberrant splicing pathogenicity (TSASP) prediction.
- Design and implement a pilot AI clinical decision support (AI-CDS) system to accelerate RGD clinical recognition and support personalized diagnostic pathway planning.