Goal: To train deep learning algorithms on genomic biobanks to identify rare genetic variants associated with disease.
Goal: To develop a best-in-class AI-assisted clinical decision making tool for maternal preeclampsia.
Preeclampsia (PE) is a maternal hypertensive disorder that claims the lives of 50,000 mothers and 500,000 babies annually. Because its symptoms are very generic, early and accurate detection of PE is a difficult task.
Outcomes: I helped develop a best-in-class AI diagnostic aid and maternal risk stratification tool for PE to assist clinicians tailor patient care strategies. I improved model sensitivity and recall (PPV) by >10% for PE status prediction and developed and evaluated a predicted PE severity score, which demonstrated strong correlation with physician-recorded severity status (no/low/high severity). Feature analysis revealed insight into potentially novel, high-specificity biomarkers for PE diagnosis. I will present an excerpt of this at the BMEII Symposium in New York.
Goal: perform hierarchical clustering on AlphaFold-predicted structures to inform cancer target strategic priorities.
In cancer pharmacology, there is great interest in identifying common actionable features between oncogenic structures to reduce wastefulness of lead discovery.
Outcome: I scraped 600+ cancer driver proteins and hierarchically clustered them by structural similarity. I then rendered a polar dendrogram where nodes represented oncoproteins and encoded patient mutation frequency (AACR GENIE) in the node's radius. I also developed a tool to swap GENIE data with any other cancer datasets of interest (cBioPortal, CRISPRko screens, COSMIC). I documented this project's workflow over here.
Goals: develop generalizable tools to increase pharmacogenomics throughput and expidite ADME screening.
Goal: determine the mechanism of action for the TGF-B/smad peroxide resistance response in C. elegans nematodes.
The peroxide immune response is key to managing ROS load in C. elegans neurons. The TGF-B pathway mediates this response and is tightly regulated, leaving the peroxide defense mechanism of action unknown.
Outcome: I performed lifespan and behavioral assays on type I and type II TGF-B ligand/receptor RNAi nematode knockouts during peroxide exposure. I observed a signal integration phenotype between type I and type II TGF-B that causes half the double ligand knockouts to live longer than wildtype. I followed this with bulk RNA-seq and GO analysis of C. elegans smad knockouts (TGF-B receptor-binding proteins) to trace differential gene expression from TGF-B silencing. I discovered that both smad-2 and smad-4 knockouts overexpressed lon-1, which is found abundantly in peroxide-sensing neurons and implicated in negative regulation of the TGF-B pathway.
Goal: develop bioanalytic software systems to increase throughput for immunotherapeutic discovery.
* asterisk indicates co-first author.
David D'Onofrio*, Shrey Patel*, Nandini Samanta*, Mikio Tada*, Vivek Kanpa*. Cost-benefit analysis of non-AI and AI models implemented for predicting chief complaints. PRISM, 2024.
Birgitta Taylor-Lillquist, Vivek Kanpa, Maya Crawford, Mehdi El Filali, Julia Oakes, Alex Jonasz, Amanda Disney, Julian Paul Keenan. Preliminary Evidence of the Role of Medial Prefrontal Cortex in Self-Enhancement: A Transcranial Magnetic Stimulation Study. Brain Sciences, 2020.
Thesis supervisor: Dr. Kuan-lin Huang, Precision -Omics Lab.
Technical interests: computational genomics, machine learning, network biology, multi-omics, precision medicine
Non-technical interests: healthcare economics, public health, AI policy