ArticleBioMed research international2014
Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.Molecules (Basel, Switzerland) · 2024Review
- SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.Biomedicines · 2022Article
- CPIELA: Computational Prediction of Plant Protein-Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information.Frontiers in genetics · 2022Article
- Machine Learning Analysis for Phenolic Compound Monitoring Using a Mobile Phone-Based ECL Sensor.Sensors (Basel, Switzerland) · 2021Article
- Article
- Large-Scale Protein Interactions Prediction by Multiple Evidence Analysis Associated With an In-Silico Curation Strategy.Frontiers in bioinformatics · 2021Article
- Graph-based prediction of Protein-protein interactions with attributed signed graph embedding.BMC bioinformatics · 2020Article
- An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model.International journal of molecular sciences · 2019Article
- An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation.Frontiers in genetics · 2019Article
- Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection.Molecular genetics and genomics : MGG · 2018Article
- Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.Molecules (Basel, Switzerland) · 2017Article
- Sequence-based prediction of protein protein interaction using a deep-learning algorithm.BMC bioinformatics · 2017Article
- PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.International journal of molecular sciences · 2017Article
- Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding.BMC bioinformatics · 2016Article
- Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.BioMed research international · 2016Article
- Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.BioMed research international · 2016Article
- Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines.BioMed research international · 2015Article
- Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set.BMC bioinformatics · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.
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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.