Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology

When evaluations aren't trustworthy, entire research programs can chase mirages. Objective benchmarks and independent assessment have repeatedly catalyzed progress across computational biology, from protein structure prediction to variant interpretation and single‑cell analysis. This workshop gathers leaders of community challenges and benchmarking infrastructures together with domain experts to provide a contemporary view of how to design trustworthy evaluations, why blind prediction matters, and where standards, infrastructure, and policy must evolve to meet the demands of AI‑driven biology. We summarize the motivation and scope of the workshop; provide background on methodological and infrastructural advances that enable rigorous benchmarking; highlight invited speakers' contributions; and outline anticipated outcomes and community calls‑to‑action.

Authors

Institutions

Publication Details

Published
2025-12-01
DOI
https://doi.org/10.1142/9789819824755_0065
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology

Serghei Mangul, Steven E. Brenner, Predrag Radivojac, Gaia Andreoletti
Scientific Computing and Data Management
article

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology

Serghei Mangul, Steven E. Brenner, Predrag Radivojac, Gaia Andreoletti
article en

Abstract

When evaluations aren't trustworthy, entire research programs can chase mirages. Objective benchmarks and independent assessment have repeatedly catalyzed progress across computational biology, from protein structure prediction to variant interpretation and single‑cell analysis. This workshop gathers leaders of community challenges and benchmarking infrastructures together with domain experts to provide a contemporary view of how to design trustworthy evaluations, why blind prediction matters, and where standards, infrastructure, and policy must evolve to meet the demands of AI‑driven biology. We summarize the motivation and scope of the workshop; provide background on methodological and infrastructural advances that enable rigorous benchmarking; highlight invited speakers' contributions; and outline anticipated outcomes and community calls‑to‑action.

Vol. 31
Northeastern University (US), Sage Bionetworks (US), University of California, Berkeley (US)
Openalex Percentile: Top 64%
Scientific Computing and Data Management
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.