Leveraging Heterogeneity to Accelerate Translational Medicine
We are taking advantage of advances in machine learning to turn the traditional paradigm in biomedical research on its head. Instead of limiting heterogeneity in data, as a traditional biomedical research experiment does, we are embracing heterogeneity in data. We firmly believe that biological and technical heterogeneity in data is a blessing in disguise and can accelerate translational medicine. We have repeatedly demonstrated that biological and technical heterogeneity in data, coupled with novel machine learning methods, is not only desired, but required to identify robust disease signatures across patient populations that are diagnostic, prognostic, therapeutic, and mechanistic.
We take advantage of the biological and technical heterogeneity that exists in diseases, methods that generated the data, and genetically distinct cohorts throughout the world. The results from our “in silico” experiments lead to new hypothesis generation, and “wet lab” validation of the findings. This approach has already been shown to work in sepsis, influenza vaccination and infection, cancer, autoimmune diseases, and transplant rejection.
Systems immunology of Pfizer-BioNTech mRNA vaccine (Preprint 2021)
EpiTOF identifies novel mechanism of cellular differentiation (Nature Immunology 2021)
Conserved host response to viral infection predicts severity (Immunity 2021)
Translation of 3-gene signature for TB diagnosis in a point-of-care cartridge (J Clin Microbio 2021)
Interferon-independent signature of SLE (JCI Insight 2020)
Predicting poor outcome in fibrosis (Lancet Respir Med 2019)
Predicting progression to severe dengue (Cell Reports 2019)
EpiTOF: platform for single-cell epigenome profiling (Cell 2018)
Systems immunology analysis of Mtb infection (Nature 2018)
Increasing accuracy of cellular deconvolution (Nature Communications 2018)
NK cells predict susceptibility to influenza prior to exposure (Genome Medicine 2018)
Predicting response to anti-TNF prior to treatment (Gut 2018)
Predict response to influenza vaccine (Sci Immunology 2017)
Ligand discovery for tumor-derived T cell receptors (Cell 2017)
Framework for leveraging heterogeneity (Nucleic Acids Research 2017)
Distinguishing viral and bacterial infections (Sci Transl Med 2016)
Diagnosis of tuberculosis (Lancet Respir Med 2016)
Host response to viral infections (Immunity 2015)
Diagnosis of sepsis in trauma patients (Sci Transl Med 2015)
Drug repurposing for transplant rejection (J Exp Med 2013)
