Ben Logsdon works as a senior scientist at Sage Bionetworks, where he leads the integrated network working group for the Accelerating Medicine Partnership – Alzheimer’s Disease (AMP-AD) consortium along with a lead for the bioinformatics working group within the Molecular Mechanisms of the Vascular Etiology of Alzheimer’s Disease (M²OVE-AD) Consortium. He also leads the Sage team’s efforts on variant prioritization to be pushed into new mouse models of late onset Alzheimer’s disease within the context of the MODEL-AD consortium. Dr. Logsdon has made multiple contributions to the fields of statistical genetics, genome-wide association studies, systems biology, machine learning, and cancer biology through the development of new computational techniques to extract signals from high dimensional data. Specifically, he pioneered the application of approximate inference techniques for Bayesian regression models in the context of genome-wide association studies and reconstructing gene coexpression networks. In the context of the Alzheimer's Disease research he is leading efforts to construct more robust and reproducible models of interactions among genes based on transcriptomic data, along with building better community resources to accelerate the identification of novel therapeutic targets for Alzheimer’s disease through the AMP-AD Knowledge portal.
Identifying Alzheimer’s disease driver genes, personalized medicine, penalized regression, variational Bayes approximate inference, rare variant association mapping, statistical genomics, cancer genomics, Gaussian graphical models, computational biology