A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

Keyphrases

• Functional Properties 100%
• Supervised Learning Method 100%
• Semi-supervised Learning 100%
• Functional Gene Network 100%
• Synthetic Genetic Interactions 100%
• Topological Properties 100%
• Weighted Functional 42%
• Semi-supervised Method 42%
• Graph-based Semi-supervised Learning 42%
• Limited Training Data 28%
• Support Vector Machine 28%
• Saccharomyces Cerevisiae 28%
• Gene Pairs 28%
• Genetic Interaction 28%
• Supervised Learning Classifiers 28%
• Biologists 14%
• Protein-coding Genes 14%
• Phenotypic Change 14%
• Gene Function 14%
• Gene Expression Data 14%
• Interacting Proteins 14%
• Protein-protein Interaction 14%
• Protein Complex 14%
• Gene mutation 14%
• Prediction Accuracy 14%
• Computational Approach 14%
• Systems Biology Approach 14%
• Benchmark Dataset 14%
• Unlabeled Data 14%
• Source Code 14%
• Labeled Data 14%
• Supervised Approach 14%
• Supervised Classifier 14%
• Complex Expressions 14%
• Synthetic Genetic Array 14%
• Design of Experiments 14%
• Computational Systems Biology 14%
• Combinatorial Explosion 14%
• Combined mutation 14%
• Functional Linkage 14%
• Pathway Structure 14%
• Non-interaction 14%
• High Coverage 14%
• Protein Pairs 14%
• Modifier Screen 14%
• Genetic Interaction Profiles 14%

Computer Science

• Learning Approach 100%
• Semisupervised Learning 100%
• Support Vector Machine 33%
• Experimental Result 16%
• Gene Expression Data 16%
• Unlabeled Data 16%
• Computational Approach 16%
• Candidate Gene 16%
• Computational System 16%

Biochemistry, Genetics and Molecular Biology

• Gene Interaction 100%
• Gene Network 100%
• Support Vector Machine 18%
• Protein-Protein Interaction 18%
• Gene Linkage 9%
• Gene Mutation 9%
• Candidate Gene 9%
• Protein Complexes 9%
• Gene Expression Data 9%
• Gene Function 9%
• Synthetic Genetic Array 9%
• Computational Systems Biology 9%