A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).

Dana Mary Varghese, T Athulya, Vikash K Mohani, Shandar Ahmad

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Article in Methods in molecular biology (Clifton, N.J.), 2025. 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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4 authors.

Dana Mary VargheseSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

T AthulyaSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

Vikash K MohaniSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

Shandar AhmadSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India. shandar@jnu.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein function prediction from sequence, structure, gene expression profiles, and published literature are needed to understand all biological processes. Natural language processing of biological text and large language model (LLM)-based encoding of sequence and structure opens powerful paths to rapid function annotation and novel training models. In this survey, we take a look at the available models for function prediction, especially the NLP- and LLM-based models. The survey highlights the major advances made and the ground that still needs to be covered to automate the process of function prediction from two major sources namely protein sequences and published research documents.

Indexed as

Computational BiologyNatural Language ProcessingProteinsHumansLarge Language ModelsProteinsFunction predictionGene ontologiesLarge language modelsNatural language processingProtein function

Identifiers

PMID40601260

DOI10.1016/j.cels.2023.10.002

What OpenQuestion holds

Textmetadata

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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.