Benjamin Scott Glicksberg

This paper presents a drug repurposing framework using in vivo transcriptomics to expand indications by matching drug signatures with disease signatures, successfully recovering established uses of semaglutide and proposing new ones.

This paper uses temporal modeling of Alzheimer's disease progression with metabolomics data to identify novel genetic loci associated with disease speed, independent of traditional case-control associations.

This paper creates a database of space-flown drugs and identifies spaceflight-related gene targets to advance pharmacogenomics for space missions.

This study shows that BCI-838, a mGluR2/3 antagonist, mimics exercise's benefits on neurogenesis, behavior, and molecular pathways in an Alzheimer's mouse model.

This study identifies sex-specific immune dysregulation in Alzheimer's Disease through meta-analysis of gene expression in brain and blood samples.

This study evaluates the prognostic value of polygenic risk scores for schizophrenia and finds they do not improve outcome prediction beyond clinical features.

The paper introduces the Pleiotropic Variability Score (PVS) to quantify the phenomic associations of genomic variants using semantic reasoning and validates it with electronic health records.

This study links genomic data with EHRs to identify health outcomes enriched in sub-populations, revealing both known and novel associations.

OCTAD enables virtual screening of therapeutics for cancer patient groups using gene expression features, prioritizing potent drug candidates for experimental testing.

This study identifies drug-comorbidity interactions in T2D using EMR data and genetic validation, finding protective effects of certain drugs against comorbidities.

This study evaluates the genomic similarity between breast cancer cell lines and metastatic samples, finding significant differences and highlighting organoids as better models.

The paper introduces an R package for calculating fragility index in clinical trials and extends the method to survival analysis and logistic regression.

The paper evaluates using GTEx normal tissue samples as references for cancer research and finds that a deep learning approach can effectively select appropriate normal samples for disease signature creation.

This study uses machine learning to prioritize small molecules for drug repositioning by analyzing chemical properties and leveraging public drug databases.

This study uses computational drug repositioning and gene expression analysis to identify potential therapies for Nipah Virus infection.

The paper uses machine learning on electronic medical records to identify comorbidities and medications associated with Lyme disease and its treatment outcomes.

The paper identifies a common collagen disease in Puerto Ricans using identity-by-descent mapping and genomic data from a health system.

The paper integrates GWAS and conditional eQTL data to identify schizophrenia risk loci with co-localization, finding 40 loci and six with conditional eQTL co-localization.