Lana X. Garmire

STDrug improves drug repurposing by integrating spatial transcriptomics with machine learning to prioritize patient-specific therapies.

SPIN integrates spatial omics data for population-scale clinical prediction and biological discovery, achieving high accuracy in dementia and breast cancer outcomes.

The paper uses single-cell imaging features to predict breast cancer patient survival and identifies seven survival subtypes with distinct cellular profiles.

This study benchmarks integration methods for spatial transcriptomics data, showing that combining highly variable and spatially variable genes improves clustering performance.

DeepProg predicts patient survival subtypes using multi-omics data, identifying two optimal subtypes and improving risk-stratification across multiple cancers.

The paper develops a network-based method to predict drug sensitivity for cancer patients using cell line and omics data, identifying effective drug-patient matches.

The paper introduces SSrGE, a method using SNVs from scRNA-seq to identify subpopulations and link genotype-phenotype relationships, showing better accuracy than gene expression.

This paper develops a deep learning model integrating multi-omics data to predict survival in liver cancer, identifying two patient subgroups with significant survival differences.

Cox-nnet improves prognosis prediction using high-throughput transcriptomics data with higher accuracy than existing methods and provides biological insights.