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Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology
Andreoletti, Gaia Mangul, Serghei Radivojac, Predrag Brenner, Steven E
Andreoletti, Gaia
Mangul, Serghei
Radivojac, Predrag
Brenner, Steven E
Supervisor
Department
Computational Biology
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Journal article
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http://creativecommons.org/licenses/by/4.0/
Language
English
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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.
Citation
G. Andreoletti, S. Mangul, P. Radivojac, S.E. Brenner, "Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology," Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, vol. 31, pp. 865-868, 2025, https://doi.org/10.1142/9789819824755_0065.
Source
Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing
Conference
Keywords
31 Biological Sciences, 3101 Biochemistry and Cell Biology, Artificial Intelligence, Benchmarking, Computational Biology, Humans, Reproducibility of Results
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Publisher
World Scientific Publishing