Area of research
Radiology, Nuclear Medicine and Imaging · Artificial Intelligence
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, Liver Disease Diagnosis and Treatment, and Fungal and yeast genetics research.
AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases
Artificial Intelligence–Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study
Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH
Combination Therapies Including Cilofexor and Firsocostat for Bridging Fibrosis and Cirrhosis Attributable to NASH
Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis
Evolutionary principles of modular gene regulation in yeasts
Proto-genes and de novo gene birth
Comparative analysis of mycobacterium and related actinomycetes yields insight into the evolution of mycobacterium tuberculosis pathogenesis
A functional selection model explains evolutionary robustness despite plasticity in regulatory networks
Comparative analysis of mycobacterium and related actinomycetes yields insight into the evolution of mycobacterium tuberculosis pathogenesis
DSpace@MIT (Massachusetts Institute of Technology) 2012cited by 0position: middle