Tomiko Oskotsky

GENOAR enables scalable, AI-compatible single-cell transcriptomic analysis of over 54 million cells from humans and mice, outperforming human experts in data retrieval and enabling new biological insights.

The paper identifies simvastatin and primaquine as potential endometriosis treatments using human assembloids, showing significant reversal of disease-related gene expression pathways.

This study uses DNA methylation data to predict endometriosis status and menstrual cycle phase, finding strong performance for cycle phase and moderate performance for endometriosis.

This study identifies simvastatin and primaquine as potential therapeutics for endometriosis through computational drug repositioning, showing reduced pain and gene expression changes in animal models and lower disease risk in EMR data.

The paper presents a benchmarking challenge to predict Alzheimer's disease neuropathology from single-nucleus transcriptomics using human and AI models, finding that top methods achieve near-perfect categorical staging predictions.

CDRPipe integrates single-cell and bulk transcriptomic perturbation data to improve drug repurposing predictions by combining complementary resources.

The paper introduces the Intolerome Database, a curated resource of genes essential for human viability, to help prioritize genomic variants in cases of stillbirth, pregnancy loss, and neonatal death.

This study evaluates large language models' ability to generate predictive modeling code for reproductive health using standardized datasets, finding R code generation more successful than Python and some models matching top DREAM challenge teams.

The paper investigates how age, sex, and APOE genotype affect hippocampal cell-type-specific gene expression and communication in mice, revealing sex-divergent effects of APOE4.

This study compares transcriptomes of early onset preeclampsia and placenta accreta, finding inverse gene expression in key cell types and pathways at the maternal-fetal interface.

The paper introduces SIMCO, a single-cell digital twin framework for evaluating PTL prevention drugs, identifying tetrahydrofolate, maprotiline, and aspirin-lansoprazole as top candidates.

The paper introduces anticlustering to balance batches and minimize batch effects in high-throughput sequencing, showing improved balance compared to existing methods.

The paper uses single-cell transcriptomics to analyze endometrial cells from endometriosis patients, revealing altered cellular features and immune communication networks across the menstrual cycle.

The paper identifies a cell-type-directed combination therapy for Alzheimer's disease using single-cell transcriptomics and drug databases, showing improved memory and reduced pathologies in a mouse model.

This paper harmonizes microbiome studies by placing amplicon sequence variants (ASV) on a common phylogenetic tree, improving community relationship accuracy compared to closed-OTU methods.

The paper identifies fetal sex-specific differentially methylated CpG sites and regions in placental DNA methylation data from 532 samples.

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

A computational drug repositioning pipeline identifies potential therapeutics for COVID-19 by matching SARS-CoV-2 gene expression signatures to drug profiles.

This paper identifies 13 biomarkers for rheumatoid arthritis using cross-tissue transcriptomic data and machine learning, validated across multiple datasets and showing clinical utility.