EC-Bench: a benchmark for enzyme commission number prediction

Authors

Sara Davoudi
Christopher S. Miller (ORCID: https://orcid.org/0000-0002-9448-8144)
F Banaei-Kashani
Christopher Scott Henry

Institutions

Argonne National Laboratory (US)
University of Denver (US)
University of Colorado Denver (US)

Publication Details

Journal: Bioinformatics Advances
Published: 2026-01-01
DOI: https://doi.org/10.1093/bioadv/vbag004
Primary Topic: Machine Learning in Bioinformatics
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

EC-Bench: a benchmark for enzyme commission number prediction

Sara Davoudi, Christopher S. Miller, F Banaei-Kashani, Christopher Scott Henry

Jan 1, 2026

Bioinformatics Advances

Machine Learning in Bioinformatics

article

EC-Bench: a benchmark for enzyme commission number prediction

Sara Davoudi, Christopher S. Miller, F Banaei-Kashani, Christopher Scott Henry

January 1, 2026article en

Abstract

No abstract available for this paper.

Bioinformatics AdvancesVol. 6(1)

Argonne National Laboratory (US), University of Denver (US), University of Colorado Denver (US)

Openalex Percentile: Top 92%

Machine Learning in Bioinformatics

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

EC-Bench: a benchmark for enzyme commission number prediction — Sara Davoudi, Christopher S. Miller, et al. · Bioinformatics Advances (2026) | TGRS Research Map | TGRS