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Source note3 min read

Plausibility-Driven Prioritization of Candidate Biomedical Annotations

Source

arXiv

arxiv.org/abs/2607.20163v1

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Plain-Language Brief

The paper presents a framework using biomedical knowledge graphs (bioKGs) to estimate the plausibility of candidate biomedical annotations, aiming to guide experts in prioritizing them for manual curation. The approach trains binary classifiers with a community-based negative sampling strategy and introduces plausibility measures that consider multiple relationships between biological entities.

What the Document Says

  • The rapid growth of biomedical knowledge creates a bottleneck in validating automatically generated annotations due to the need for costly expert review.
  • Machine learning techniques can support this process by exploiting biomedical knowledge graphs (bioKGs) which capture biological entities and their functional associations.
  • A framework is proposed that leverages bioKGs to estimate the plausibility of candidate annotations and guide expert curation.
  • Starting from knowledge graph embeddings, they train relation-specific binary classifiers using a community-based negative sampling strategy for reliable confidence estimates.
  • Plausibility measures are introduced that combine classifier confidence, classifier reliability, and the semantic context provided by alternative relationships involving the same pair of biological entities.
  • The proposed approach accounts for multiple biologically meaningful relations that may coexist between the same entities.
  • Experimental results on five large bioKGs demonstrate that the negative sampling strategy improves classifier robustness, increasing balanced accuracy by an average of 5.8%.
  • Plausibility measures outperform classifier confidence alone, enabling more effective prioritization of candidate annotations for expert review.
  • The use of bioKGs improves the efficiency of AI-assisted biomedical curation while preserving expert control over the final annotation assessment.

Key Concepts Mentioned

  • biomedical knowledge
  • biological annotations
  • biomedical curation
  • machine learning
  • biomedical knowledge graphs (bioKGs)
  • binary classifiers
  • negative sampling strategy
  • plausibility measures
  • balanced accuracy

Claims Made by the Document

  • Machine learning techniques can support the validation of biomedical annotations.
  • The proposed framework leverages bioKGs to estimate plausibility and guide expert curation.
  • The negative sampling strategy improves classifier robustness, increasing balanced accuracy by an average of 5.8%.
  • Plausibility measures outperform classifier confidence alone.

Evidence Used

  • The text mentions that machine learning techniques can support the validation of biomedical annotations.
  • The text describes a framework that leverages bioKGs to estimate plausibility and guide expert curation.
  • The experimental results demonstrate that the negative sampling strategy improves classifier robustness, increasing balanced accuracy by an average of 5.8%.
  • The experimental results show that plausibility measures outperform classifier confidence alone.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

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Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

The rapid growth of biomedical knowledge has made the validation of automatically generated biological annotations a major bottleneck in biomedical curation. While computational methods can rapidly produce large numbers of candidate annotations, determining which are biologically valid still requires costly expert review. Prioritizing these candidates before manual curation has therefore become a fundamental challenge. Machine learning techniques can support this process by exploiting biomedical knowledge graphs (bioKGs), which capture biological entities and their functional associations. In this work, we propose a framework that leverages bioKGs to estimate the plausibility of candidate annotations and guide expert curation. Starting from knowledge graph embeddings, we train relation-specific binary classifiers using a community-based negative sampling strategy to obtain reliable confidence estimates. We then introduce a family of plausibility measures that combine classifier confidence, classifier reliability, and the semantic context provided by alternative relationships involving the same pair of biological entities. Unlike conventional confidence estimation, the proposed approach explicitly accounts for multiple biologically meaningful relations that may coexist between the same entities. Experimental results on five large bioKGs demonstrate that the proposed negative sampling strategy consistently improves classifier robustness, increasing balanced accuracy by an average of 5.8%. Moreover, the plausibility measures outperform classifier confidence alone, enabling more effective prioritization of candidate annotations for expert review. Overall, our results show that the use of bioKGs improves the efficiency of AI-assisted biomedical curation while preserving expert control over the final annotation assessment.