Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

HODC-AES v1.0.0: A Reliability-Aware Benchmark for Higher-Order Drug-Combination Adverse-Event Signal Prediction

Authors: Elahi, Asif; Mahruf, Nur Hossain; Kabir, Raihan; Farvez, Anowar Hossen;

HODC-AES v1.0.0: A Reliability-Aware Benchmark for Higher-Order Drug-Combination Adverse-Event Signal Prediction

Abstract

HODC-AES is a reliability-aware benchmark for higher-order drug-combination adverse-event signal prediction in computational pharmacovigilance. This v1.0.0 release contains the full archival benchmark package, including processed benchmark artifacts, generated contrastive negatives, prediction outputs, paper-ready tables and figures, documentation, release metadata, and SHA256 checksums. The benchmark is built from public FAERS quarterly reports and is designed for evaluation under random and temporal validation, hard-negative protocols, calibration analysis, top-k alert-budget evaluation, and temporal generalization-gap analysis. Main release statistics:- 973,898 positive signal candidates- 1,500,000 generated contrastive negatives- Five negative protocols: random, drug-count-matched, frequency-matched, one-drug-replacement, and hybrid-hard- Zero known-positive overlap among generated negatives- 165,878 unique drug combinations- 12,311 unique reaction terms Important: HODC-AES treats FAERS-derived labels as reported pharmacovigilance signal candidates, not as confirmed causal clinical drug-drug interactions. The resource is intended for research use only and not for clinical decision-making.

Related Organizations
Keywords

drug safety, machine learning, benchmark dataset, FAERS, computational pharmacovigilance, higher-order drug combinations, top-k evaluation, adverse-event signal prediction, temporal validation, calibration

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average