ArticleFrontiers in genetics2020
Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.PLoS computational biology · 2026Article
- Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Inconsistency among evaluation metrics in link prediction.PNAS nexus · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.Scientific reports · 2024Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
- Normalized L3-based link prediction in protein-protein interaction networks.BMC bioinformatics · 2023Article
- A novel graph mining approach to predict and evaluate food-drug interactions.Scientific reports · 2022Article
- ProteinPrompt: a webserver for predicting protein-protein interactions.Bioinformatics advances · 2022Article
- Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient.Frontiers in genetics · 2021Article
- Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment.Frontiers in genetics · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions are the foundations of cellular life activities. At present, the already known protein-protein interactions only account for a small part of the total. With the development of experimental and computing technology, more and more PPI data are mined, PPI networks are more and more dense. It is possible to predict protein-protein interaction from the perspective of network structure. Although there are many high-throughput experimental methods to detect protein-protein interactions, the cost of experiments is high, time-consuming, and there is a certain error rate meanwhile. Network-based approaches can provide candidates of protein pairs for high-throughput experiments and improve the accuracy rate. This paper presents a new link prediction approach "Sim" for PPI networks from the perspectives of proteins' complementary interfaces and gene duplication. By integrating our approach "Sim" with the state-of-art network-based approach "
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.