Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
A generic minimization random allocation and blinding system on web
Journal of Biomedical Informatics
| Hi-index | 0.00 |
Mutations in the human FBNI gene are known to be associated with the Marfan syndrome, an autosomal dominant inherited multi-systemic connective tissue disorder. However, in the absence of solid genotype-phenotype correlations, the identification of an FBNI mutation has only little prognostic value. We propose a bioinformatics framework for the mutated FBNI gene which comprises the collection, management, and analysis of mutation data identified by molecular genetic analysis (DHPLC) and data of the clinical phenotype. To query our database at different levels of information, a relational data model, describing mutational events at the cDNA and protein levels, and the disease's phenotypic expression from two alternative views, was implemented. For database similarity requests, a query model which uses a distance measure based on log-likelihood weights for each clinical manifestation, was introduced. A data mining strategy for discovering diagnostic markers, classification and clustering of phenotypic expressions was provided which enabled us to confirm some known and to identify some new genotype-phenotype correlations.