PathRanker R PackagePathRanker is an R-package for extraction and analysis of the most active metabolic pathways through gene expression data. For full functionality the package requires these additional R-packages as well as graphviz version 2.20.3.1 (link)
Journal PublicationsHancock T , Mamitsuka H , "Boosted Network Classifiers for Local Feature Selection." (2012) IEEE Transactions in Neural Networks; 23(11) 1793-1804 Hancock T , Wicker N , Takigawa I , Mamitsuka H , "Identifying Neighborhoods of Coordinated Gene Expression and Metabolite Profiles." (2012) PLoS ONE 7(2): e31345. doi:10.1371/journal.pone.0031345 Hancock, T., Takigawa, I., Mamitsuka H., "Mining metabolic pathways through gene expression ", (2010) Bioinformatics; doi: 10.1093/bioinformatics/btq344 Hancock, T.,Mamitsuka H., "A Markov classification model for metabolic pathways", (2009) Algorithms for Molecular Biology, Volume 5(10) Hancock, T., Mamitsuka H., “Active Pathway Identification and Classification with Probabilistic Ensembles”, (2009) Genome Informatics, Volume 22. Hancock, T., Mamitsuka H., “Semi-Supervised Graph Partitioning with Decision Trees”, (2008), Genome Informatics Volume 20. Donald, D., Coomans, D., Everingham, Y., Cozzolino, D., Gishen, M., Hancock, T. (2006), "Adaptive wavelet modeling of a nested 3 factor experimental design in NIR chemometrics" Chemometrics and Intelligent Laboratory Systems, 82, 122-129. Donald, D., Hancock, T., Coomans, D., and Everingham, Y. (2006), "Bagged super wavelet reduction for boosted prostate cancer classification of seldi-tof mass spectral serum profiles", Chemometrics and Intelligent Laboratory Systems, 82, 2-7. Deconinck E., Hancock T., Coomans D., Massart D.L., Heyden Y.V., Classification of drugs in absorption classes using the classification and regression trees (CART) methodology Journal of pharmaceutical and biomedical analysis 39 (1), 91-103 Hancock, T., Put, R., Coomans D., Heyden, Y. V., and Everingham, Y. (2005), "A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies", Journal of Chemometrics and Intelligent Laboratory Systems, 76, 185-196. Smyth, C., Coomans, D., Everingham, Y., Hancock, T. (2005), "Auto-associative Multivariate Regression Trees for Cluster Analysis", Chemometrics and Intelligent Laboratory Systems, 80, 120-129. Conference PublicationsHancock, T., Mamitsuka H., "Boosted Optimization for Network Classification", Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (JMLR: Workshop and Conference Proceedings, Vol. 9) Hancock, T., Mamitsuka H., “A Markov classification model for metabolic pathways”, (2009), Workshop on Algorithms in Bioinformatics (WABI). Hancock, T., Mamitsuka H., “Active Pathway Identification and Classification with Probabilistic Ensembles”, (2009) International Workshop on Bioinformatics and Systems Biology (IBSB). Hancock, T., Mamitsuka H., “Semi-Supervised Graph Partitioning with Decision Trees”, (2008) International Workshop on Bioinformatics and Systems Biology (IBSB). Hancock, T., Coomans, D., and Everingham, Y. (2004), "A Combination of Genetic Algorithms and Boosted Regression Trees for the Prediction RNase L Levels in Subsets of Chronic Fatigue", Proceedings of the International Biometrics Conference (IBC) 2004. Smyth, C., Coomans, D., Everingham, Y., Hancock, T., "Multivariate regression trees for cluster analysis", (2004) International Symposium on Business and Industrial Statistics (ISBIS). Sim, N., Coomans, D., Atkinson, I., Hancock, T., "Using Grid computing to achieve timely data mining", (2004) International Symposium on Business and Industrial Statistics (ISBIS). Hancock, T., Coomans, D., and Everingham, Y., "Supervised hierarchical clustering using CART", (2003) International Congress on Modeling and Simulation (MODSIM). Book ChaptersHancock, T., Takigawa, I., Mamitsuka H.,
Book Title: “Data Mining for Systems Biology”, Hancock, T., Smyth, C. D. Coomans, C. Smyth, I. Lee, Hancock T.,
Book Title: “Comprehensive Chemometrics: Chemical and Biochemical Data Analysis”, Posters / Unrefereed ConferencesHancock, T., Mamitsuka, H., “Imposing Network Structures on Feature Selection with Experimental Data”, (2012), Sapporo Workshop on Machine Learning and Applications to Biology (MLAB) Hancock T , Wicker N , Takigawa I , Mamitsuka H , "Identifying Neighborhoods of Coordinated Gene Expression and Metabolite Profiles." (2012) International Workshop on Bioinformatics and Systems Biology (IBSB). Kirwan G. M., Hassell K., Hancock, T., Niere, J., Nugegoda, D., Goto S., Adams M. J., "NMR Metabonomic Profiling Using O2PLS", (2012), 13th Conference on Chemometrics in Analytical Chemistry (CAC). Hancock, T., Mamitsuka, H., “Are Features of Biological Networks Sparse or Dense?”, (2011), International Workshop on Bioinformatics and Systems Biology (IBSB). Hancock, T., Mamitsuka, H., “Novel Algorithms To Identify Differentially Expressed Features Within Biological Networks”, (2010) Conference of the Japanese Society of Bioinformatics (JSBI). Hancock, T., Takigawa, I., Mamitsuka, H., “PathRanker: Mining metabolic pathways through gene expression”, (2010), International Workshop on Bioinformatics and Systems Biology (IBSB). Hancock, T., Mamitsuka H., “Using Local Reaction Structure To Build A Global Metabolic Network Classifier”, (2009) Genome Informatics Workshop (GIW). Hancock, T., Mamitsuka H., "Active Pathway Identification and Classification with Probabilistic Ensembles”, (2008) Conference of the Japan Society for Bioinformatics (JSBI). Hancock, T., Shiga, M., Mamitsuka, H., and Coomans, D., “Modular Sub-graph Partitioning with Decision Trees”, (2007), Japanese Society for Bioinformatics (JSBI) Coomans, D., Hancock, T., Aeberhard, S., Everingham, Y., Donald, D., Smyth, C., Sim, N., and Llellewyn, L., "FIDO: Tailored software for large scale data mining in Bioinformatics / Chemoinformatics", (2004) International Conference on Chemometrics and Bioinformatics (CCBA). PHD DissertationHancock, T., “Multivariate Consensus Trees: Tree-based clustering and profiling for mixed data types”, (2007) James Cook University. |