towards causal interpretability in deep learning for parkinson's detection from voice data
dec 2025princeton journal of interdisciplinary research (pjir) · vol. 1, issue 2
aniruth ananthanarayanan, sudeep senivarapu, anishsairam murari
view paper →research
publications
symbolic regression for mycophenolic acid dosage prediction in kidney transplant recipients
2025medrxiv
sudeep senivarapu, aniruth ananthanarayanan, anishsairam murari, benjamin z. hu, alexander z. sha
view paper →computational protocol for organism-level pathogenicity classification from bacterial genomes using machine learning
forthcomingbook chapter · methods in molecular biology
sudeep senivarapu, tallon coxe, anishsairam murari, rajeev k. azad. a reproducible computational protocol for classifying bacterial pathogenicity from genome sequences using k-mer features, random forests, svms, and convolutional neural networks.
datasets
an extended dataset of extracted acoustic features for voice-based detection of parkinson's disease
2025figshare
extended acoustic feature dataset for parkinson's disease voice analysis research.
view dataset →presentations
oral & poster presentation
2025bmes annual meeting 2025 · biomedical engineering society
view contribution → view poster →poster presentation (accepted)
2025rice360 conference for global health technologies · rice university
media coverage
new scientist parkinson's detection from voice data → bbc science focus parkinson's diagnosis years early → science alert voice-based parkinson's detection → yahoo news voice analysis for parkinson's detection →research interests
computational biology
protein language modeling
interpretable ml
symbolic genomics
disease mechanism inference
ai for health equity