Data Mining and Bioinformatics

Bioinformatics at Microsoft Research.- A Novel Approach for Effective Learning of Cluster Structures with Biological Data Applications.- Subspace Clustering of Microarray Data Based on Domain Transformation.- Bayesian Hierarchical Models for Serial Analysis of Gene Expression.- Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data.- A Biological Text Retrieval System Based on Background Knowledge and User Feedback.- Automatic Annotation of Protein Functional Class from Sparse and Imbalanced Data Sets.- Bioinformatics Data Source Integration Based on Semantic Relationships Across Species.- An Efficient Storage Model for the SBML Documents Using Object Databases.- Identification of Phenotype-Defining Gene Signatures Using the Gene-Pair Matrix Based Clustering.- TP+Close: Mining Frequent Closed Patterns in Gene Expression Datasets.- Exploring Essential Attributes for Detecting MicroRNA Precursors from Background Sequences.- A Gene Structure Prediction Program Using Duration HMM.- An Approximate de Bruijn Graph Approach to Multiple Local Alignment and Motif Discovery in Protein Sequences.- Discovering Consensus Patterns in Biological Databases.- Comparison of Modularization Methods in Application to Different Biological Networks.