ArticleFrontiers in genetics2018
Reconstruction of the Protein-Protein Interaction Network for Protein Complexes Identification by Walking on the Protein Pair Fingerprints Similarity Network.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 16 citations in OpenAlex.
- Ion channel traffic jams: the significance of trafficking deficiency in long QT syndrome.Cell discovery · 2025Review
- Spatiotemporal constrained RNA-protein heterogeneous network for protein complex identification.Briefings in bioinformatics · 2024Article
- Article
- Small protein complex prediction algorithm based on protein-protein interaction network segmentation.BMC bioinformatics · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- Systems Medicine Design for Triple-Negative Breast Cancer and Non-Triple-Negative Breast Cancer Based on Systems Identification and Carcinogenic Mechanisms.International journal of molecular sciences · 2021Article
- An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning.Frontiers in genetics · 2021Article
- Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks.Frontiers in genetics · 2021Article
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying protein complexes from protein-protein interaction networks (PPINs) is important to understand the science of cellular organization and function. However, PPINs produced by high-throughput studies have high false discovery rate and only represent snapshot interaction information. Reconstructing higher quality PPINs is essential for protein complex identification. Here we present a Multi-Level PPINs reconstruction (MLPR) method for protein complexes detection. From existing PPINs, we generated full combinations of every two proteins. These protein pairs are represented as a vector which includes six different sources. Then the protein pairs with same vector are mapped to the same fingerprint ID. A fingerprint similarity network is constructed next, in which a vertex represents a protein pair fingerprint ID and each vertex is connected to its top 10 similar fingerprints by edges. After random walking on the fingerprints similarity network, each vertex got a score at the steady state. According to the score of protein pairs, we considered the top ranked ones as reliable PPI and the score as the weight of edge between two distinct proteins. Finally, we expanded clusters starting from seeded vertexes based on the new weighted reliable PPINs. Applying our method on the yeast PPINs, our algorithm achieved higher
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.