Daniel Himmelstein

Clinical Advancement Forecasting

This paper develops a statistical learning framework to predict clinical trial success for target-disease pairs using longitudinal evidence, outperforming existing scores and identifying high-potential targets.

The probability of edge existence due to node degree: a baseline for network-based predictions

The paper introduces a network permutation framework to assess how node degree influences edge prediction in biomedical networks and provides an edge prior as a baseline for performance evaluation.

Hetnet connectivity search provides rapid insights into how two biomedical entities are related

The paper introduces hetnet connectivity search to find meaningful relationships between biomedical entities in heterogeneous networks without requiring supervised training data.

Unifying the Identification of Biomedical Entities with the Bioregistry

The paper introduces the Bioregistry, an open metaregistry that integrates and expands existing biomedical entity registries to improve interoperability and data reuse.

Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

The paper improves knowledge graph expansion by reusing label functions for multiple edge types in biomedical abstracts.

Analysis of ISCB honorees and keynotes reveals disparities

The paper analyzes disparities in recognition of computational biology researchers based on gender, name origin, country, and race/ethnicity.

Sequential compression of gene expression across dimensionalities and methods reveals no single best method or dimensionality

The paper evaluates multiple compression methods and dimensionalities on gene expression data, finding no single best approach but showing benefits from ensemble methods across different dimensions.

Opportunities And Obstacles For Deep Learning In Biology And Medicine

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

This paper uses a biomedical knowledge network to predict drug-disease treatment pairs and prioritize drug repurposing candidates.