Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory


Mining data from gene expression data could be a hot analysis topic and direction of bioinformatics. Gene choice and sample classification are significant analysis trends, thanks to the large quantity of genes and small size of samples in gene expression data. Rough set theory has been successfully applied to gene choice, as it can select attributes without redundancy. To enhance the interpretability of the chosen genes, some researchers introduced biological information. During this paper, we have a tendency to initial employ neighborhood system to deal directly with the new info table formed by integrating gene expression knowledge with biological information, that can simultaneously present the information in multiple perspectives and do not weaken the knowledge of individual gene for choice and classification. Then, we tend to offer a completely unique framework for gene selection and propose a vital gene selection technique based on this framework by using reduction algorithm in rough set theory. The proposed method is applied to the analysis of plant stress response. Experimental results on three data sets show that the proposed method is effective, because it can select significant gene subsets while not redundancy and achieve high classification accuracy. Biological analysis for the results shows that the interpretability is well.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :A Dynamic Multiagent Genetic Algorithm for Gene Regulatory Network Reconstruction Based on Fuzzy Cognitive MapsABSTRACT:In order to reconstruct giant-scale gene regulatory networks (GRNs) with high accuracy, a robust
PROJECT TITLE :Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy LogicABSTRACT:This paper proposes a modification to the analytic hierarchy process (AHP) to select the foremost informative genes that
PROJECT TITLE :bLARS: An Algorithm to Infer Gene Regulatory NetworksABSTRACT:Inferring gene regulatory networks (GRNs) from high-throughput gene-expression information is an important and difficult problem in systems biology.
PROJECT TITLE :Ontology-Based Prediction and Prioritization of Gene Functional AnnotationsABSTRACT:Genes and their protein merchandise are essential molecular units of a living organism. The knowledge of their functions is vital
PROJECT TITLE :Extracting Cross-Ontology Weighted Association Rules from Gene Ontology AnnotationsABSTRACT:Gene Ontology (GO) may be a structured repository of concepts (GO Terms) that are associated to at least one or additional

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry