Héctor Corrada Bravo

The paper introduces SIGnature, a framework for scoring gene importance using attributions from scRNA-seq foundation models, and demonstrates its utility in identifying novel associations of the MS1 signature with hyperinflammatory conditions.

The paper introduces Decima, a model that predicts cell type- and condition-specific gene expression from DNA sequence using single-cell RNA sequencing data.

This study uses dynamic caging to link live-cell behavior with transcriptional responses across perturbations, revealing new insights into immune and inflammatory gene regulation.

This paper introduces CompBioBench, a benchmark for evaluating agentic systems in computational biology, showing strong performance across diverse tasks.

PaSCient learns patient-level disease representations from scRNA-seq data, enabling disease classification and downstream analyses like dimensionality reduction and patient subgroup discovery.

Nona is a multimodal masking framework that unifies sequence-to-function models, language models, and generative models for functional genomics, improving predictions and revealing privacy vulnerabilities in ATAC-seq data.

The paper introduces an open-source ecosystem for microbiome data science using Bioconductor, supporting modular workflows for analyzing complex, multi-modal datasets.

Tangram2 maps cell-to-cell communication by integrating spatial transcriptomics with single-cell RNA sequencing to identify gene expression changes driven by neighboring cell types.

SPEX provides a web-based platform for spatial omics analysis, enabling modular and user-friendly processing of spatial transcriptomics and proteomics data.

PerturbView enables highly multiplexed optical pooled screens in primary cells and tissues by improving barcode amplification, revealing regulators of NFκB signaling and enabling spatial transcriptomics integration.

The paper introduces SCimilarity, a metric learning framework for querying and comparing single-cell RNA-seq data across the human body, revealing commonalities in fibrosis-associated macrophages.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

gEAR provides a community-driven portal for multi-omic data visualization, analysis, and sharing across species and data types.

The paper introduces epivizFileServer, a Python library for querying and manipulating genomic data from indexed files, improving exploratory analysis efficiency.

Terminus groups transcripts based on inferential uncertainty to enable robust transcript-level or group-level analysis from RNA-seq data.

CDGnet recommends targeted cancer therapies using a network approach that integrates biological pathway information and patient-specific molecular data.

scGAIN imputes missing gene expressions in scRNA-seq data using GANs, improving clustering and matching with bulk RNA-seq.

The paper introduces an R package for handling 16S rRNA databases and marker-gene survey data, providing consistent interfaces for database comparison and data structure development.

The paper addresses compositional bias in sparse sequencing count data by proposing an empirical Bayes normalization approach and clarifying assumptions of existing scaling methods.

The paper evaluates 16S rRNA marker gene analysis pipelines using a mixture dataset of human stool samples, finding DADA2 better captures specific features than Mothur and QIIME.

Yanagi segments transcriptomes to enable fast and interpretable analysis of alternative splicing and gene-level expression using pseudo-alignment.

Metaviz provides an interactive web tool for exploring and visualizing metagenomic data through heatmaps, bar plots, and PCA scatter plots.

This paper introduces a normalization method for genomic data that accounts for biological differences in distributions while reducing technical variation.

The paper introduces a method using smoothing splines to analyze longitudinal microbial marker-gene data, identifying time periods of differential abundance across sample groups.