ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Artificial Intelligence Powers Protein Functional Annotation.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Protein functional annotation is essential for understanding biological processes, disease mechanisms, and enzyme activities, yet experimental validation remains costly and low-throughput. With the rapid development of Artificial Intelligence (AI), a wide range of computational approaches have been proposed to infer protein functions. This review systematically examines methods for annotating Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. These are two complementary systems that capture different aspects of protein functions. Based on these two systems, we first synthesize existing approaches into six general modeling paradigms with a clear, structured framework. Then, we introduce GO and EC in a parallel manner, consisting of representative methods, commonly used evaluation metrics, prediction scenarios, and task-specific challenges. Finally, we outline emerging opportunities and future directions aimed at achieving more accurate, context-dependent, and high-resolution protein functional annotation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.