Demo Data from EDBT 2026 / reviewers in the wild / expert
Yebin Ryu
dblp:437/0667
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 56% Medical and health informatics · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › data integration
evidence integration |
1.0 | 1 | 2026 | IDAP: an integrated literature- and knowledge-graph-driven evidence prioritization pipeline for precision oncology · Bioinform. 2026 |
Medical and health informatics › precision medicine
precision oncology |
1.0 | 1 | 2026 | IDAP: an integrated literature- and knowledge-graph-driven evidence prioritization pipeline for precision oncology · Bioinform. 2026 |
Bioinformatics and computational biology
knowledge graph |
0.3 | 1 | 2026 | IDAP: an integrated literature- and knowledge-graph-driven evidence prioritization pipeline for precision oncology · Bioinform. 2026 |
Methods — techniques the papers use, named apart from their topics
percentile normalization · 1.0literature mining · 1.0knowledge graph · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | IDAP: an integrated literature- and knowledge-graph-driven evidence prioritization pipeline for precision oncologyabstractMOTIVATION: Advances in tumor sequencing enable routine detection of dozens to hundreds of somatic alterations per patient, yet only a minority can be linked to established therapeutic evidence. Curated resources such as OncoKB provide high-quality variant-drug annotations but remain limited in coverage, particularly for rare or low-frequency variants. This coverage gap motivates computational frameworks that can integrate curated, literature-derived, graph-based, and clinical-trial evidence to prioritize therapeutic hypotheses for expert review. RESULTS: We developed the Integrated Drug Annotation Pipeline (IDAP), a modular framework that combines four complementary evidence streams: curated variant-drug associations from OncoKB, literature-derived gene-drug mention counts from PubMed abstracts, graph-based drug prioritization using a TxGNN-derived biomedical knowledge graph, and cancer-specific clinical-trial evidence from ClinicalTrials.gov. Given a cancer type and a MAF file, IDAP generates patient-level reports summarizing detected variants, ranked therapeutic hypotheses, supporting evidence layers, and relevant clinical trials. Evaluated across five cancer types (n = 50 samples), IDAP expanded evidence-linked therapeutic hypotheses beyond curated databases alone. Among patients without OncoKB recommendations (26/50), IDAP identified a median of 87 candidate drugs (range: 2-473). To reduce cross-source scale imbalance, the final ranking used within-sample percentile normalization with fixed bonuses for curated evidence, multi-source support, and trial linkage. Under this revised ranking, 24/50 top-ranked candidates were supported by at least two evidence sources and 44/50 had associated ClinicalTrials metadata. In an exploratory external CIViC comparison, IDAP recovered at least one matched CIViC-supported therapy in 28/41 eligible samples, with 13/41 appearing within the top 10 candidates. These outputs are intended to support evidence triage and translational interpretation rather than direct treatment recommendation. AVAILABILITY AND IMPLEMENTATION: IDAP is freely available at https://github.com/joonan-lab/IDAP-pipeline, with full documentation at https://joonan-lab.github.io/IDAP-pipeline. An archived snapshot of the code used in this study is deposited on Zenodo (DOI: https://doi.org/10.5281/zenodo.19301367). Yebin Ryu, Ha-Eun Jeong, Joon-Yong An |
Bioinform. | 1 |