Topic overview
Representation learning for biological datasets and literature.
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5 works
Papers in this area
We combine publication metadata, author neighborhoods and dataset signals to improve retrieval quality for computational biology teams working across papers, code and experiments.
We curate paper, code and dataset benchmarks for biology-focused language models under provenance and reproducibility constraints.
We trace dataset reuse across scholarly graphs to improve auditability, discovery and replication planning.
We align code repositories, datasets and papers with provenance-aware linking that survives version churn and renamed assets.
We study open-weight assistants that combine paper summaries, dataset cards and experiment context for biology teams.
People in this topic
Senior Lecturer
Works on review quality, moderation design and institutional trust systems for online science.
Open to collaboratePostdoctoral Researcher
Works on literature-scale retrieval and scientific representation learning.
Open to collaborateComputational Biology Fellow
Curates multimodal biology benchmarks and literature-aware evaluation pipelines.
Heads-down modeIndustry Research Scientist
Bridges dataset-centric ML with reproducibility tooling.
Heads-down modeResearch Software Engineer
Links papers, repositories and datasets with reproducible provenance infrastructure.
Open to collaborate