SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD: hACE2 interactions in SARS-CoV-2.
Priya GuptaDebasisa MohantyPublished in: Briefings Bioinform. (2021)
Keyphrases
- protein protein interactions
- protein interaction
- machine learning
- interacting proteins
- wet lab
- protein protein
- predicting protein
- computational methods
- high throughput
- predicting protein protein interactions
- drug design
- gene ontology
- biomedical literature
- biological processes
- biological knowledge
- functional modules
- biological data
- protein interaction networks
- genomic data
- protein function prediction
- computational approaches
- structural properties
- network topology
- hiv protease
- machine learning methods
- statistical methods
- cellular processes
- protein complexes
- protein function
- protein interaction data
- high precision
- experimentally determined
- systems biology
- biological networks
- text mining
- dna binding
- data analysis
- data mining
- network analysis
- information extraction
- ppi networks
- real time
- low cost
- microarray
- mass spectrometry
- data integration
- feature selection
- learning algorithm