The Prasath Lab uses AI/Data Science Tools for Biomedical Informatics
We combine mathematics, computer science, and statistics to solve challenging biomedical informatics problems with special emphasis in pediatric domain.
We use image processing, computer vision, machine and deep learning for large-scale biomedical imaging and multi-modal data.
We do science that is collaborative and crosses disciplines. We follow a culture that is integrative and inclusive.
News
Collaborative work with Douglas Mast Lab appeared in Proceedings of Meetings on Acoustics (POMA), 2024: Deep learning-enhanced 3D echo decorrelation imaging for monitoring radiofrequency ablation in ex vivo human liver. Kudos to Elmira Ghahramani Zarajabad (Current at Weill Cornell) for leading this research!
Collaborative work with Jasbir Dhaliwal Lab appeared in Gastroenterology, 2024: Machine learning-based prediction of pediatric ulcerative colitis treatment response using diagnostic histopathology. Kudos to Xiaoxuan (Ruby) Liu for leading this project!
Collaborative work with Jasbir Dhaliwal Lab appeared in Computers in Biology and Medicine, 2024: Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Kudos to Xiaoxuan (Ruby) Liu who spearheaded this project!
Under the LungMap Data Coordinating Center (DCC) initiative we have made multi-omics data "findable, accessible, interoperable and re-usable (FAIR)" - to establish and share best practices, coordinate metadata annotation for community. Check our publication, 2024: LungMAP Portal Ecosystem: Systems-level exploration of the lung appeared in American Journal of Respiratory Cell and Molecular Biology. (https://lungmap.net, https://omero.lungmap.net, http://app.lungmap.net/app/azimuth-lung-cell-cards-human)
Collaborative work with Alexey Porallo Lab appeared in Microbiome, 2023: MGS2AMR: A gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile. (Github, Youtube) Kudos to PJ van Camp (Currently at HMS, Harvard) for leading this research!
🎉🎉 5 years of Prasath Lab!
Achievement unlocked - Crossed 50 Tb of data across the lab projects and counting !
Collaborative work with Emily Miraldi, Leah Kottyan, Artem Barski, Matt Weirauch labs appeared in PLoS Computational Biology, 2023: maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks, Kudos to Tareian Cazares, Faiz Rizvi from Miraldi lab, and Balaji Iyer from our lab!
(ScienceBlog, Portal, Github)
Collaborative work with Nathan Salomonis Lab appeared in Nature Communications, 2023: Decision level integration of unimodal and multimodal single cell data with scTriangulate. (Github, PyPI) Kudos to Guangyuan (Frank) Li from Salomonis lab!
Collaborative work with Manan Shah, Deepak Jain (Rutgers), Kevin Dufendach appeared in Pediatric Research, 2023: Artificial intelligence in bronchopulmonary dysplasia - Current research and unexplored frontiers. Kudos to Manan Shah who spearheaded this project!
Collaborative work with Bruce Aronow lab appeared in Briefings in Bioinformatics, 2022: CellDrift: Inferring perturbation responses in temporally sampled single-cell data. Kudos to Kang Jin from Aronow lab!
(Github)
Collaborative work with Manan Shah (Rutgers), Derek Shu, Yizhao Ni, Andrew Schapiro, Kevin Dufendach appeared in Applied Clinical Informatics, 2021: Machine learning for detection of correct peripherally inserted central catheter tip position from radiology reports in infants. Kudos to Manan Shah who spearheaded this project!
Collaborative work with Nathan Salomonis Lab appeared in Briefings in Bioinformatics, 2021: DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Kudos to Guangyuan (Frank) Li from Salomonis lab, and Balaji Iyer from our lab!
(ScienceBlog, Featured-Research @ CCHMC 2022, Portal, Github)
Collaborative work with Daniel Lucas, Nathan Salomonis, and Lee Grimes labs appeared in Nature, 2021: In situ mapping identifies distinct vascular niches for myelopoiesis. Kudos to Jizhou Zhang, Qingqing Wu from Lucas lab! Check out the eye-candy imaging data 😍
News/Student Achievements
🎉 Congratulations to Xiaoxuan (Ruby) Liu, UC Computer Science, MS student on successfully defending her thesis. She continues as a BMI, CCHMC PhD student from Fall 2022!
🎉 Congratulations to Aronow Lab's BMI, CCHMC PhD student Kang Jin on 2nd Best Poster Prize for our collaborative work on CellDrift at the Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI), 30th Conference on Intelligent Systems for Molecular Biology (ISMB), 2022!
🎉 Congratulations to our Lab's BMI, CCHMC PhD student Smruti Deoghare on getting the Graduate Student Government (GSG) Research Fellowship 2021 - Advancement of Interdisciplinary Research Award!
Collaborative efforts from our lab towards smartphone + machine learning driven COVID-19 diagnostics with Aashish Priye (Chemical and Environmental Engineering, UC) featured in ZDnet news!
🎉 Congratulations to our Lab's EECS, UC PhD student Balaji Iyer on getting the Graduate Student Government (GSG) Research Fellowship 2020 - Advancement of Interdisciplinary Research Award!
🎉 Congratulations to our Lab's BMI, CCHMC PhD student Smruti Deoghare on getting the second place in the 3 Minute Thesis (3MT) Contest at the University of Cincinnati, 2020.
🎉 Congratulations to our Lab's BMI, CCHMC PhD student Smruti Deoghare on getting nominated from the University of Cincinnati for The Midwestern Association of Graduate Schools (MAGS) Excellence in Teaching Award, 2020. See her "Teaching in Action" Youtube video!
🎉 Congratulations to our Lab's (first) MS students Mr. Srinivasa Siddhartha Selagamsetty (MS, EECS, UC, joined a start-up), and Mr. Harshith Bondada (MS, EECS, UC, joined Proctor & Gamble company) on their new jobs!
🎉 Congratulations to our Lab's international exchange MS students Ms. Asami Yonekura (EE, Mie, Japan), Mr. Kiichi Fukuma (EE, Mie, Japan) on their new jobs!
We are looking for strong and motivated students (or remote interns), see also Student Success Page!
Grants
Active:
Received a Cystic Fibrosis Foundation (CFF) grant with Rhonda Szczesniak for HEAL-CF: Genome-sociome informed risk (G-SIR) prediction tools for enhanced clinical management and promotion of health equity across the lifespan (HEAL). (2024 - 2028)
Received a Center for Pediatric Genomics (CpG) grant from the Cincinnati Children’s Research Foundation (CCRF) with Emily Miraldi for transcription factor binding prediction from ATAC-seq and nascent transcription with deep learning models. (2022 - 2023)
Our lab is part of a CCHMC-Technion's Bridge to Next Gen Medicine Program grant with Patrick Ruck on AI for POC ultrasound diagnostic clinical workflow improvement. (2022-2023).
Our lab is part of a Crohns & Crohn's & Colitis Foundation (CCF) CRIA grant with Jasbir Dhaliwal on AI for prediction of pediatric ulcerative colitis with imaging, clinical, and genomics data. (2022 - 2024)
Our lab is part of a CCHMC PROCTER grant with Jasbir Dhaliwal on machine learning for pediatric ulcerative colitis disease course prediction with histopathological imaging data. (2022 - 2024)
Our lab is part of a NIH R01 grant with Daniel Lucas on hematopoietic stem cells engraftment using in situ imaging. (2022-2026)
Our lab is part of an multi-disciplinary CCHMC ARC grant and initiative with Jonathan Dillman, Sam Brady, Lili He, Ryan Moore, Alex Towbin to create an "Image-based AI Core Structure" - Creation of an Enterprise-Wide Multidisciplinary Image-Focused Artificial Intelligence Infrastructure and Core for the development of an image-based AI core at the institute. (2021-2022)
Our lab is part of a Digestive Health Center (DHC) grant with Jasbir Dhaliwal on bench-to-bedside research in pediatric digestive disease for leveraging machine learning to delineate histologic features that are predictive of pediatric ulcerative colitis disease. (2021 - 2022)
Our lab is part of the Pediatric Precision Brain Health Center CCHMC ARC grant with Bruce Aronow, Alexis Mitalpunkt, Mary Anne McMahon for the development of longitudinal video/imaging informatics for the precision rehabilitation of traumatic brain injury (TBI)/cerebral palsy (CP) population. (2021-2022)
Our lab is part of a NIH/NIAID U01 grant with Emily Miraldi on dynamic regulatory network modeling with deep learning. (2020 - 2025)
Our lab is part of a U2CTR grant Data Management Coordinating Center (DMCC) NIH/NCATS Rare Diseases Clinical Research Network (RDCRN) for the consistent data integration workflows, management, storage, analysis of various imaging data from 20+ different consortia spread across the USA. (2019 - 2024)
Our lab is part of a U24 grant LungMap Data Coordinating Center (DCC) from NIH/NHLBI for the imaging data collection, integration, and analysis of various consortia. (2019 - 2024)
Past:
Received a Center for Pediatric Genomics (CpG) grant from the Cincinnati Children’s Research Foundation (CCRF) with Emily Miraldi for methods to improve insight from (sc)ATAC-seq for regulatory networks and genetics. (2020 - 2021)
Received a Center for Pediatric Genomics (CpG) grant from the Cincinnati Children’s Research Foundation (CCRF) with Krishna Roskin for the development of a database and machine learning tools to accelerate immunoglobulin and TCR bioinformatics. (2019 - 2020)
Our lab was part of the Pediatric Cell Atlas CCHMC ARC grant with Nathan Salomonis, Bruce Aronow for the development of imaging informatics for the atlas. The pediatric atlas is part of a broader international consortium, the Human Cell Atlas (HCA). (2019-2020)
Our lab was part of the project Drone-based DNA Analysis with Aashish Priye (PI, Chemical and Environmental Engineering, UC), Soryong Chae (Co-PI, Environmental Engineering Science) in the Digital Futures Platforms Development - Anchor Initiative (DF 19-1) grant at UC. (2019 - 2020)
Our lab was part of a Ohio Third Frontier Grant with Ryan Moore to develop automated anatomical segmentation using AI tools for cardiology. (2019 - 2020)
Our lab was part of an CCHMC ARC grant with Jonathan Dillman, Andrew Trout for the development of machine learning tools for liver ultrasound images. (2018 - 2020)
We thank the research funding support from Cincinnati Children's Research Foundation (CCRF), CCHMC, CCTST, UC, DHC, CpG, CCF, NIH, and NSF. None of the opinions expressed here represent the CCHMC, University of Cincinnati or the funding agencies. © Prasath Lab, CCHMC, USA, 2021-2024.