ArticlePloS one2011
Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed, 173 citations in OpenAlex.
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
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- To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification.Sensors (Basel, Switzerland) · 2022Article
- Identifying Functions of Proteins in Mice With Functional Embedding Features.Frontiers in genetics · 2022Article
- Identify RNA-associated subcellular localizations based on multi-label learning using Chou's 5-steps rule.BMC genomics · 2021Article
- Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment.Frontiers in genetics · 2021Article
- A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma.Frontiers in genetics · 2021Article
- A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem.Frontiers in genetics · 2021Article
- Use of a Network-Based Method to Identify Latent Genes Associated with Hearing Loss in Children.Frontiers in cell and developmental biology · 2021Article
- Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting.Molecular genetics and genomics : MGG · 2020Article
- Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer.Journal of cellular and molecular medicine · 2020Article
- Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding.Frontiers in genetics · 2020Article
- Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature ExtractionCurrent genomics · 2019Article
- Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms.Gene therapy · 2019Article
- iN6-methylat (5-step): identifying DNA NMolecular genetics and genomics : MGG · 2019Article
- Inferring novel genes related to colorectal cancer via random walk with restart algorithm.Gene therapy · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.Database : the journal of biological databases and curation · 2019Article
- Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia.Molecular therapy. Nucleic acids · 2018Article
- A computational method using the random walk with restart algorithm for identifying novel epigenetic factors.Molecular genetics and genomics : MGG · 2018Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner. METHODOLOGY/PRINCIPAL
findingsAlthough many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%. CONCLUSIONS/SIGNIFICANCE: The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.
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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.