Donald Geman

CellCover Defines Marker Gene Panels Capturing Developmental Progression in Neocortical Neural Stem Cell Identity

CellCover identifies marker gene panels for cell types by solving a set-covering problem, outperforming differential expression methods in capturing cell-specific signals and developmental progression.

Mapping spatial gradients in spatial transcriptomics data with score matching

This paper introduces SLOPER, a method to learn spatial gradients from spatial transcriptomics data, improving the identification of tissue organization and spatially variable gene modules.

GeneCover: A Combinatorial Approach for Label-free Marker Gene Selection

GeneCover selects label-free marker genes using gene-gene correlations, effectively distinguishing cell states in scRNA-seq and spatial transcriptomics data.

Longitudinal changes in brain metabolites in healthy subjects and patients with first episode psychosis (FEP): a 7-Tesla MRS study

This study uses 7-Tesla MRS to track longitudinal changes in brain metabolites in FEP patients and healthy controls, identifying potential trait and state markers for psychosis.

Using Biological Constraints to Improve Prediction in Precision Oncology

The paper develops mechanistic classifiers using biological constraints to improve the robustness and interpretability of gene signatures in cancer prediction.

Human olfactory neuronal cells through nasal biopsy: molecular characterization and utility in brain science

This study characterizes olfactory neuronal cells from nasal biopsies using single-cell and bulk RNA sequencing, showing their neuronal similarity and utility in brain disorder research.

Efficient Representations of Tumor Diversity with Paired DNA-RNA Aberrations

The paper introduces a method to represent tumor diversity using paired DNA-RNA aberrations, identifies minimal coverings of aberrant gene pairs, and quantifies heterogeneity through entropy measures across cancer types.

An R Package for Divergence Analysis of Omics Data

The paper introduces an R package for divergence analysis of omics data, demonstrating its use with Cancer Genome Atlas data.

Splice Expression Variation Analysis (SEVA) for Inter-tumor Heterogeneity of Gene Isoform Usage in Cancer

SEVA identifies differential heterogeneity in gene isoform usage between cancer subtypes using RNA-seq data.