@article {2152, title = {Transitive closure and metric inequality of weighted graphs: detecting protein interaction modules using cliques.}, journal = {International Journal of Data Mining and Bioinformatics}, volume = {1}, year = {2006}, month = {2006 Jun 2}, pages = {162-77}, abstract = {

We study transitivity properties of edge weights in complex networks. We show that enforcing transitivity leads to a transitivity inequality which is equivalent to ultra-metric inequality. This can be used to define transitive closure on weighted undirected graphs, which can be computed using a modified Floyd-Warshall algorithm. These new concepts are extended to dissimilarity graphs and triangle inequalities. From this, we extend the clique concept from unweighted graph to weighted graph. We outline several applications and present results of detecting protein functional modules in a protein interaction network.

}, keywords = {Algorithms, Computational Biology, Fungal Proteins, Models, Protein Binding, Protein Interaction Mapping, Systems Biology, Theoretical}, issn = {1748-5673}, doi = {10.1007/s12021-010-9090-x}, author = {Ding, Chris and He, Xiaofeng and Xiong, Hui and Peng, Hanchuan and Holbrook, Stephen R} } @article {2158, title = {Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {27}, year = {2005}, month = {2005 Aug}, pages = {1226-38}, abstract = {

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Cluster Analysis, Computer Simulation, Computer-Assisted, Diagnosis, Humans, Information Storage and Retrieval, Models, Neoplasms, Numerical Analysis, Pattern Recognition, Statistical}, issn = {0162-8828}, doi = {10.1007/s12021-010-9090-x}, author = {Peng, Hanchuan and Long, Fuhui and Ding, Chris} } @article {2159, title = {Minimum redundancy feature selection from microarray gene expression data.}, journal = {Journal of Bioinformatics and Computational Biology}, volume = {3}, year = {2005}, month = {2005 Apr}, pages = {185-205}, abstract = {

How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. We propose a minimum redundancy - maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 6 gene expression data sets: NCI, Lymphoma, Lung, Child Leukemia, Leukemia, and Colon. Improvements are observed consistently among 4 classification methods: Naive Bayes, Linear discriminant analysis, Logistic regression, and Support vector machines. SUPPLIMENTARY: The top 60 MRMR genes for each of the datasets are listed in http://crd.lbl.gov/\ cding/MRMR/. More information related to MRMR methods can be found at http://www.hpeng.net/.

}, keywords = {Algorithms, Automated, Discriminant Analysis, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Pattern Recognition}, issn = {0219-7200}, doi = {10.1007/s12021-010-9090-x}, author = {Ding, Chris and Peng, Hanchuan} }