Name
Abstract | ||
|---|---|---|
Protein-protein interactions (PPIs) are central to the most cellular processes. Although PPIs have been generated exponentially from experimental methods ranging from high throughput protein sequences to the crystallized structures of complexes, only a fraction of interactions have been identified. It's challenging to integrate diverse datasets for computational methods. In order to predict PPIs over diverse datasets, we proposed a full Bayesian network model. First, we investigated the dihedral angle of atom C-alpha to describe flexible and rigid regions of protein structures and then design domain-domain interaction (DDI) template library to predict DDIs by the dihedral angle of atom C-alpha. Hence, both of them are viewed as the features of a full Bayesian Network (BN). Second, we used two encoding methods on sequences. The two encoding sequences can reflect both biological and physiochemical properties of proteins. Third, we also viewed gene co-expression as a feature of the BN model. Finally, we used receiver operating characteristic (ROC) to assess the performance compared to the Support Vector Machine (SVM) model. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BIBMW.2012.6470198 | BIBM Workshops |
Keywords | Field | DocType |
crystallized complex structures,crystal structure,belief networks,gene coexpression,cellular biophysics,crystallisation,high-throughput protein sequences,full bayesian network,physiochemical properties,dihedral angle,biological properties,atom c-alpha,encoding method,protein-protein interactions,encoding sequences,encoding sequence,genetics,proteins,computational methods,encoding,domain-domain interaction template library,diverse datasets,bn model,biochemistry,biology computing,molecular biophysics,molecular configurations,support vector machine,bayesian network,protien structure,protein-protein interaction,receiver operating characteristics,high throughput protein sequence,protein structure,full bayesian network model,cellular processes,protein sequence,support vector machines,sensitivity analysis,protein protein interactions | Protein–protein interaction,Protein sequencing,Computer science,Support vector machine,Bayesian network,Artificial intelligence,Molecular biophysics,Bioinformatics,Machine learning,Dihedral angle,Encoding (memory),Protein structure | Conference |
ISBN | Citations | PageRank |
978-1-4673-2744-2 | 0 | 0.34 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Li | 1 | 3 | 5.46 |
| Chun-Mei Liu | 2 | 245 | 41.30 |
| William Southerland | 3 | 15 | 6.23 |
| Legand Burge | 4 | 29 | 9.60 |
| Kyung Dae Ko | 5 | 3 | 1.79 |