# Plausibility-Driven Prioritization of Candidate Biomedical Annotations

arXiv: https://arxiv.org/abs/2607.20163 · HTML: https://mlsanity.com/paper/2607.20163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1708.06816/) (2017)
- [An Open-Source Knowledge Graph Ecosystem for the Life Sciences](https://mlsanity.com/paper/2307.05727/) (2023)
- [KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling](https://mlsanity.com/paper/2112.09340/) (2021)
- [Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks](https://mlsanity.com/paper/2305.19979/) (2023)
- [RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules](https://mlsanity.com/paper/2312.00183/) (2023)
- [Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion](https://mlsanity.com/paper/2411.08165/) (2024)
- [The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models](https://mlsanity.com/paper/2409.04103/) (2024)
- [RNA-KG v2.0: An RNA-centered Knowledge Graph with Properties](https://mlsanity.com/paper/2508.07427/) (2025)
- [Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2405.16902/) (2024)
- [Unifying biomedical knowledge in a modern multimodal graph](https://mlsanity.com/paper/2604.27269/) (2026)

## Abstract (arXiv)

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.