Michael is a computer science PhD student focused on AI for healthcare, specifically challenges in operationalizing AI models into practice and training large-scale pre-trained models ("foundation models").

Honors & Awards

  • NSF Graduate Research Fellowship, NSF

All Publications

  • Construction of disease-specific cytokine profiles by associating disease genes with immune responses. PLoS computational biology Liu, T., Wang, S., Wornow, M., Altman, R. B. 2022; 18 (4): e1009497


    The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response-usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING's high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an "Inflammation Score" that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery.

    View details for DOI 10.1371/journal.pcbi.1009497

    View details for PubMedID 35404985

  • Interregional Transfers for Pandemic Surges. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America Michelson, K. A., Rees, C. A., Sarathy, J., VonAchen, P., Wornow, M., Monuteaux, M. C., Neuman, M. I. 2021; 73 (11): e4103-e4110


    BACKGROUND: Hospital inpatient and intensive care unit (ICU) bed shortfalls may arise due to regional surges in volume. We sought to determine how interregional transfers could alleviate bed shortfalls during a pandemic.METHODS: We used estimates of past and projected inpatient and ICU cases of coronavirus disease 2019 (COVID-19) from 4 February 2020 to 1 October 2020. For regions with bed shortfalls (where the number of patients exceeded bed capacity), transfers to the nearest region with unused beds were simulated using an algorithm that minimized total interregional transfer distances across the United States. Model scenarios used a range of predicted COVID-19 volumes (lower, mean, and upper bounds) and non-COVID-19 volumes (20%, 50%, or 80% of baseline hospital volumes). Scenarios were created for each day of data, and worst-case scenarios were created treating all regions' peak volumes as simultaneous. Mean per-patient transfer distances were calculated by scenario.RESULTS: For the worst-case scenarios, national bed shortfalls ranged from 669 to 58 562 inpatient beds and 3208 to 31 190 ICU beds, depending on model volume parameters. Mean transfer distances to alleviate daily bed shortfalls ranged from 23 to 352 miles for inpatient and 28 to 423 miles for ICU patients, depending on volume. Under all worst-case scenarios except the highest-volume ICU scenario, interregional transfers could fully resolve bed shortfalls. To do so, mean transfer distances would be 24 to 405 miles for inpatients and 73 to 476 miles for ICU patients.CONCLUSIONS: Interregional transfers could mitigate regional bed shortfalls during pandemic hospital surges.

    View details for DOI 10.1093/cid/ciaa1549

    View details for PubMedID 33038215