ReviewBiomolecules2022
Protein Function Analysis through Machine Learning.
Review in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The family of glutathione peroxidase proteins and their role against biotic stress in plants: a systematic review.Frontiers in plant science · 2025Pooled it
- Multi-omics integration and machine learning identify a novel gene signature consisting of CCDC141, CHI3L2, RIMKLB, and PDLIM7 in bronchopulmonary dysplasia associated with neutrophil-driven immune dysregulation.Clinics (Sao Paulo, Brazil) · 2026Article
- Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach.BMC plant biology · 2025Article
- Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.Frontiers in bioengineering and biotechnology · 2025Review
- ESM-scan-A tool to guide amino acid substitutions.Protein science : a publication of the Protein Society · 2024Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Integrating Computational Design and Experimental Approaches for Next-Generation Biologics.Biomolecules · 2024Review
- SUMOylation at the crossroads of gut health: insights into physiology and pathology.Cell communication and signaling : CCS · 2024Review
- A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine learning methods.Scientific reports · 2024Article
- PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications.Scientific data · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.Proteomes · 2023Review
- Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning.BMC medical informatics and decision making · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein-ligand binding, including allosteric effects, protein-protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.
Indexed as
Identifiers
What OpenQuestion holds
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.