ArticleBioinformatics (Oxford, England)2018
deepNF: deep network fusion for protein function prediction.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed.
- Predicting membrane protein localization by deep learning on structure and chemistry.Protein science : a publication of the Protein Society · 2026Article
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- REPEL - Random Embedding Perturbation for Enhanced Learning of Protein Function.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations.PloS one · 2026Article
- IMAGO: An Improved Model Based on Attention Mechanism for Enhanced Protein Function Prediction.Biomolecules · 2025Article
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- Prediction of liquid-phase separation proteins using Siamese network with feature fusion.Briefings in bioinformatics · 2025Article
- GTPLM-GO: Enhancing Protein Function Prediction Through Dual-Branch Graph Transformer and Protein Language Model Fusing Sequence and Local-Global PPI Information.International journal of molecular sciences · 2025Article
- SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction.BMC bioinformatics · 2025Article
- ICoN: integration using co-attention across biological networks.Bioinformatics advances · 2025Article
- Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.Frontiers in bioengineering and biotechnology · 2025Review
- 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
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Privacy-preserving dementia classification from EEG via hybrid-fusion EEGNetv4 and federated learning.Frontiers in computational neuroscience · 2025Article
- ProT-GFDM: A generative fractional diffusion model for protein generation.Computational and structural biotechnology journal · 2025Article
- Annotating protein functions via fusing multiple biological modalities.Communications biology · 2024Article
- Drug-target interaction prediction by integrating heterogeneous information with mutual attention network.BMC bioinformatics · 2024Article
40 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Motivation: The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly non-linear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. Results: We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting gene ontology terms of varying type and specificity. Availability and implementation: deepNF is freely available at: https://github.com/VGligorijevic/deepNF. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.