ArticleInternational journal of molecular sciences2014
Prediction of protein-protein interaction with pairwise kernel support vector machine.
Article in International journal of molecular sciences, 2014. 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.
- TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- Reconstruction ofCurrent issues in molecular biology · 2024Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Identification of all-against-all protein-protein interactions based on deep hash learning.BMC bioinformatics · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization.Journal of chemical information and modeling · 2021Article
- Large-Scale Protein Interactions Prediction by Multiple Evidence Analysis Associated With an In-Silico Curation Strategy.Frontiers in bioinformatics · 2021Article
- Identification of Proteins of Tobacco Mosaic Virus by Using a Method of Feature Extraction.Frontiers in genetics · 2020Article
- Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.International journal of molecular sciences · 2019Article
- A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences.PloS one · 2019Article
- Prediction of Protein-Protein Interactions Based on Domain.Computational and mathematical methods in medicine · 2019Article
- SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome.BMC bioinformatics · 2017Article
- Sequence-based prediction of protein protein interaction using a deep-learning algorithm.BMC bioinformatics · 2017Article
- Gene Prediction in Metagenomic Fragments with Deep Learning.BioMed research international · 2017Article
- Molecular science for drug development and biomedicine.International journal of molecular sciences · 2014Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) play a key role in many cellular processes. Unfortunately, the experimental methods currently used to identify PPIs are both time-consuming and expensive. These obstacles could be overcome by developing computational approaches to predict PPIs. Here, we report two methods of amino acids feature extraction: (i) distance frequency with PCA reducing the dimension (DFPCA) and (ii) amino acid index distribution (AAID) representing the protein sequences. In order to obtain the most robust and reliable results for PPI prediction, pairwise kernel function and support vector machines (SVM) were employed to avoid the concatenation order of two feature vectors generated with two proteins. The highest prediction accuracies of AAID and DFPCA were 94% and 93.96%, respectively, using the 10 CV test, and the results of pairwise radial basis kernel function are considerably improved over those based on radial basis kernel function. Overall, the PPI prediction tool, termed PPI-PKSVM, which is freely available at http://159.226.118.31/PPI/index.html, promises to become useful in such areas as bio-analysis and drug development.
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.