Carol J. Bult

This study shows that Svep1 is essential for proper lung development by regulating airway branching and alveolar differentiation in mice.

This study shows that cell-type-specific Dicer1 disruption accelerates KRAS-driven lung adenocarcinoma progression in mice, highlighting non-cell-autonomous miRNA-mediated effects.

This paper introduces a genomic framework to prioritize mouse strains for preclinical disease modeling, showing high sensitivity in predicting strain vulnerability to diseases.

The paper validates two genes, Cnot1 and Cdc42bpb, as causes of congenital diaphragmatic hernia in mice using patient-specific variants and shows distinct hernia phenotypes.

GenomeMUSter integrates and harmonizes mouse genetic variation data across populations to enable multi-trait, multi-population analyses and improve variant-phenotype association studies.

The paper updates the Alliance of Genome Resources' central infrastructure, adding tools and enhancing data management and curation capabilities.

The paper identifies missense variants in PLS3 causing X-linked congenital diaphragmatic hernia and body wall defects through genetic analysis and functional studies.

The paper improves a drug synergy prediction algorithm by using random forest regression and cross-validation, achieving better performance and robustness than the original method and competing algorithms.

The paper presents a pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis of tumor regions and xenograft-transplant lymphoproliferative disorder prediction.

The paper extends the Molecular Signatures Database to support mouse gene data by adding mouse-specific gene sets and improved orthology mapping resources.

The paper introduces RIP-SIR, a method to identify endogenous miRNA-target interactions across tissues and cell types.

This study analyzes NADK2 deficiency in mouse models to identify metabolic and gene expression changes, revealing similarities and differences with another neurological disease model.

The paper uses word embeddings from PubMed to predict which protein kinases are relevant for treating specific cancers using machine learning.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

The paper develops bioinformatics workflows to analyze genomic data from patient-derived xenografts, addressing challenges like separating mouse and human reads and identifying somatic mutations without paired normal DNA.

This paper develops a probabilistic model integrating sequencing and functional assay data to predict personalized drug combinations for sarcoma patients with exhausted treatment options.

This study identifies a Trpa1 splice junction variant in Diversity Outbred mice that influences late phase formalin response, linking it to pain sensitivity mechanisms.

This study quantifies intratumoral subclone dynamics in breast cancer xenografts, revealing a treatable ecology where subclone ratios predict treatment response.