ArticleScientific reports2016
FFPred 3: feature-based function prediction for all Gene Ontology domains.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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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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Who cites it
63 citing papers in PubMed.
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- Several plant self-incompatibility systems may be controlled by atypical receptor-ligand interactions.The Plant journal : for cell and molecular biology · 2026Review
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- PF-AGCN: an adaptive graph convolutional network for protein-protein interaction-based function prediction.Bioinformatics (Oxford, England) · 2025Article
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
- In Silico Analysis of s-DAPK-1: From Structure to Function and Regulation.Current issues in molecular biology · 2025Article
- Molecular modelling and gene expression analysis to probe the GT-γ trihelix transcription factors in Solanum tuberosum under drought stress.Scientific reports · 2025Article
- $\mathcal{S}$ able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm.Briefings in bioinformatics · 2025Article
- Molecular identification and functional analysis of HrpZ2, a new member of the harpin superfamily fromFrontiers in plant science · 2025Article
- Enhanced metagenomic strategies for elucidating the complexities of gut microbiota: a review.Frontiers in microbiology · 2025Review
- Multitask Learning-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).Methods in molecular biology (Clifton, N.J.) · 2025Article
- Proteome-wide analysis reveals G protein-coupled receptor-like proteins in rice (Plant signaling & behavior · 2024Article
- Structural and functional prediction, evaluation, and validation in the post-sequencing era.Computational and structural biotechnology journal · 2024Review
- Deep learning for the PSIPRED Protein Analysis Workbench.Nucleic acids research · 2024Article
- Assessing sequence variation, haplotype analysis and molecular characterisation of aspartate kinase2 (ask2) gene regulating methionine biosynthesis in diverse maize inbreds.Molecular genetics and genomics : MGG · 2024Article
- Four classic "de novo" genes all have plausible homologs and likely evolved from retro-duplicated or pseudogenic sequences.Molecular genetics and genomics : MGG · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.Bioinformatics (Oxford, England) · 2023Article
- Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.bioRxiv : the preprint server for biology · 2023Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-wide blind tests aimed at benchmarking available tools on a genomic scale. Sequence-based predictors, especially those performing homology-based transfers, remain the most popular but increasing understanding of their limitations has stimulated the development of complementary approaches, which mostly exploit machine learning. Here we present FFPred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homology with characterized proteins can provide little aid. Predictions are made by scanning the input sequences against an array of Support Vector Machines (SVMs), each examining the relationship between protein function and biophysical attributes describing secondary structure, transmembrane helices, intrinsically disordered regions, signal peptides and other motifs. This update features a larger SVM library that extends its coverage to the cellular component sub-ontology for the first time, prompted by the establishment of a dedicated evaluation category within the Critical Assessment of Functional Annotation. The effectiveness of this approach is demonstrated through benchmarking experiments, and its usefulness is illustrated by analysing the potential functional consequences of alternative splicing in human and their relationship to patterns of biological features.
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