ArticleProteome science2011
Protein complex detection with semi-supervised learning in protein interaction networks.
Article in Proteome science, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed, 50 citations in OpenAlex.
- HetFCM: functional co-module discovery by heterogeneous network co-clustering.Nucleic acids research · 2024Article
- Proteomics-Driven Biomarkers in Pancreatic Cancer.Proteomes · 2023Review
- An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks.Frontiers in genetics · 2022Article
- Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.PloS one · 2021Article
- Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks.Frontiers in genetics · 2021Article
- Identification of protein complexes and functional modules in E. coli PPI networks.BMC microbiology · 2020Article
- Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks.BMC bioinformatics · 2018Article
- Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network.Journal, genetic engineering & biotechnology · 2018Article
- Detecting complexes from edge-weighted PPI networks via genes expression analysis.BMC systems biology · 2018Article
- Predicting protein complexes using a supervised learning method combined with local structural information.PloS one · 2018Article
- NCMine: Core-peripheral based functional module detection using near-clique mining.Bioinformatics (Oxford, England) · 2016Article
- Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes.Scientific reports · 2016Article
- Predicting protein complex in protein interaction network - a supervised learning based method.BMC systems biology · 2014Article
- Applied graph-mining algorithms to study biomolecular interaction networks.BioMed research international · 2014Article
- Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.BMC bioinformatics · 2013Article
- Identifying complexes from protein interaction networks according to different types of neighborhood density.Journal of computational biology : a journal of computational molecular cell biology · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein-protein interactions (PPIs) play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection.
resultsMany people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a "semi-supervised" method to analyze the problem. In this paper, we utilize the neural network with the "semi-supervised" mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future.
conclusionsUsing a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the "semi-supervised" learning model is a promising model to detect protein complexes with more biological and topological features available.
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.