Multitask Learning-Based Approaches for Protein Function Prediction.

Soufia Bahmani, Meenal Chaudhari, Callen Carrier, Steven Garrett, Pawel Pratyush, Dukka B Kc

Abstract read

In one paragraph

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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0citing papers in PubMed

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Soufia BahmaniCollege of Computing, Michigan Technological University, Houghton, MI, USA.

Meenal ChaudhariCollege of Applied Sciences and Technology, Illinois State University, Normal, IL, USA.

Callen CarrierCollege of Computing, Michigan Technological University, Houghton, MI, USA.

Steven GarrettCollege of Computing, Michigan Technological University, Houghton, MI, USA.

Pawel PratyushGolisano College of Computing and Information Sciences, Department of Computer Science, Rochester Institute of Technology, Rochester, NY, USA.

Dukka B KcGolisano College of Computing and Information Sciences, Department of Computer Science, Rochester Institute of Technology, Rochester, NY, USA. dkcvcs@rit.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advancements in sequencing technologies have resulted in a massive growth in the number of sequences available. Only a small fraction of the proteins in UniProtKB have been functionally annotated. Understanding the roles and studying the mechanisms of newly discovered proteins is one of the most important biological problems in the post-genomic era. To address the sequence-function gap many computational methods have been developed. This chapter reviews Multitask Learning (MTL)-based approaches for protein function prediction, highlighting its potential to enhance both predictive accuracy and computational efficiency in bioinformatics. MTL utilizes shared representations to leverage common information across related tasks, improving predictive performance. Key findings reveal that MTL improves predictive performance by integrating shared features across related tasks.

Indexed as

Computational BiologyMachine LearningProteinsAlgorithmsDatabases, ProteinHumansSoftwareProteinsDeep learningDNA–protein bindingGene ontology (GO)Metal binding sitesMultitask learning (MTL)Posttranslational modification (PTM)Protein function predictionProtein language modelProtein–protein interaction (PPI)RNA–protein binding

Identifiers

PMID40728608

DOI10.1186/s12859-023-05164-9

What Socratic holds

Textmetadata

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Registered trials

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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 Socratic graph.