ArticleJournal of biosciences2019
Machine-learning techniques for the prediction of protein-protein interactions.
Article in Journal of biosciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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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.
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Who cites it
37 citing papers in PubMed, 87 citations in OpenAlex.
- Fungal infections: Classification performance and detectability with machine learning models.Scientific reports · 2026Article
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.NPJ science of food · 2025Article
- Revealing arginine-cysteine and glycine-cysteine NOS linkages by a systematic re-evaluation of protein structures.Communications chemistry · 2025Article
- Machine learning approaches for predicting craniofacial anomalies with graph neural networks.Computational biology and chemistry · 2025Article
- Protein-protein interaction prediction using bidirectional GRUs with explicit ensemble.PloS one · 2025Article
- A Complex Network of Obesity-Risk Genes Revealed by Systematic Bioinformatics and Single-Cell Transcriptomic Analyses.Journal of obesity · 2025Article
- Graph-based machine learning model for weight prediction in protein-protein networks.BMC bioinformatics · 2024Article
- Article
- Protein-Protein Interfaces: A Graph Neural Network Approach.International journal of molecular sciences · 2024Article
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.BMC bioinformatics · 2023Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Identification of novel interacts partners of ADAR1 enzyme mediating the oncogenic process in aggressive breast cancer.Scientific reports · 2023Article
- Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy.International journal of molecular sciences · 2023Article
- Graph-BERT and language model-based framework for protein-protein interaction identification.Scientific reports · 2023Article
- Identification of Relevant Protein Interactions with Partial Knowledge: A Complex Network and Deep Learning Approach.Biology · 2023Article
- Deep learning in bioinformatics.Turkish journal of biology = Turk biyoloji dergisi · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions. The two most popular supervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forest classifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI dataset confidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Several clustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for the prediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.
Indexed as
Identifiers
31502581W2966916628What Socratic 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 Socratic graph.