Bhat Lab Research
Precision Medicine for Improving Long-term Outcomes after LT
1. AI in Transplant Decision-Making
Building on our recent study, “The AI Agent in the Room: Informing Objective Decision Making at the Transplant Selection Committee” (medRxiv 2024), we are pioneering multi-agent AI systems that simulate the perspectives of different clinicians on the transplant selection committee. These systems enhance objectivity, reduce bias, and provide consistent, ethically grounded support for evaluating transplant candidates across institutions and countries.
2. HCC Evolution in Different Environments
We are investigating the molecular basis of recurrent HCC in post-transplant patients, particularly under immunosuppression. Using AI-driven integrative analysis of genomic and transcriptomic data, along with experimental models, we aim to map tumor evolution and uncover pathways that can guide image-guided therapy (e.g., nanoparticle-based approaches such as porphysomes).
3. Metabolic Disease Post-Transplant
Our research into non-alcoholic fatty liver disease (NAFLD) and post-transplant diabetes integrates omics datasets, cell free methylation profile and predictive AI models. We are developing AI-powered screening protocols that combine clinical data, elastography, and systems biology approaches. Experimental microbiome transfer models further allow us to explore causality and identify novel therapeutic targets.
4. AI-Powered Prediction of Post-Transplant Complications
With international partners, we are developing and evaluating risk calculators and neural-network–based models to predict complications such as graft fibrosis, kidney dysfunction, and cardiovascular events. These predictive tools are powered by ranked clinical and molecular features, guiding personalized preventive care, optimization of immunosuppression, and timely interventions.
5. Translational Impact
By uniting AI with systems biology, patient cohorts, digital twins, and experimental validation, our work is charting a path toward AI-informed precision transplantation. This multidisciplinary approach ensures that liver transplant recipients not only survive, but thrive long-term.
Posters Presentations
- Amongst Autoimmune Liver Diseases, Primary Sclerosing Cholangitis Demonstrates distinct Methylation Profiles on Circulating DNA. Soumita Ghosh, Cristina Baciu, Elisa Pasini, Madeline Cameron, Shani Nagler, Aisha Alawi, Aliya Gulamhusein, Gideon Hirschfield, Mamatha Bhat. The EASL Congress 2025, Amsterdam, Netherlands
- CleVER-LG: A Multimodal Machine Learning Biomarker for Non-Invasive Diagnosis of Liver Graft Injury. Soumita Ghosh, Cristina Baciu, Maryam Naghibzadeh, Amirhossein Azhie, Muhammad Enrizky Brillian, Chris Shi, Sara Naimimohasses, Sandra Fischer, Sandra Holdsworth, Arya Rahmani, Elisa Pasini, Bima J. Hasjim, Michael Brudno, Elmar Jaeckel, Daniel D. De Carvalho, Bastian Engel, Richard Taubert, Mamatha Bhat. Ajmera Annual Research Day 2025, Toronto, Canada
- Impact of Cystic Fibrosis Transmembrane Conductance Regulator Modulating Therapies on Liver Transplant Outcomes. Sara Naimimohasses, Ankit Ray, Eunice Tan, Asher Wiggins, Bima J. Hasjim, Shiyi Chen, Mamatha Bhat. Ajmera Annual Research Day 2025, Toronto, Canada
- Modeling long term cardiovascular risk for post-liver transplant patients. David Pellow, Sara Naimimohasses, Peter Maksymowsky, Erick Soo Kyun Moon, Ankit Ray, Quynh Nhi Phi, Praveen Manickavel, Michael Brudno, Mamatha Bhat. Ajmera Annual Research Day 2025, Toronto, Canada
- Azarfar G, un Y, Pasini E Humar A, Kumar D, Bhat Mamatha, Ferreira VH Using machine learning to predict COVID-19 vaccine responses in immunocompromised individuals, American Transplant Congress (ATC) 2024.
- Azarfar G, Alotaibi M, Sun Y,Husain S, Bhat, M Seyed M. Hosseini-Moghaddam, Machine Learning Models for Predicting Post-Transplant Lymphoproliferative Disease Risk: Insights from a Large-Scale Retrospective Cohort Study, American Transplant Congress (ATC) 2024.
- Nguyen-Lefebvre AT, Caldwell L, Barutcu S, Chan K, Duan K, Selzner N, Wrana J, and Bhat M. Investigating Modulatory Pathways in Human Graft Fibrosis. Ajmera Transplant Research Day, June 14th, 2024.
- Baciu C, Ghosh S, Naimimohasses S, Rahmani A, Pasini E Naghibzadeh M, Azhie A, Bhat M. Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning. Ajmera Transplant Research Day, June 14th, 2024
- S. Ghosh, K. Prayitno, S. Naimimohasses, A. Azhie, L. Gonzalez Hodar, E. Pasini, S. Satapathy, Y. Rotman, M. Bhat. Prediction and characterization of post-liver transplant metabolic dysfunction-associated steatohepatitis (MASH) using deep machine learning, ILTS 2024
- Chon J, Tan E, Sun Y,Azarfar G,Sidhu A, Jaeckel E, Merchant A, Bhat M. Prediction of Chronic Kidney disease in Liver Transplant Patients using Machine learning Ajmera Transplant Research Day, June 14th, 2024
2024 Presentations
June 2024
Authors: Acuna SA, Zhao X, Naylor K, Smith G, Chan, AW, Baxter N, Kim J, Bhat M
Title: Cumulative Exposure to Tacrolimus and Risk of Malignancy After Solid Organ Transplantation
Conference: American Transplant Congress (ATC) 2024.