Classification, Predictive Modelling, and Statistical Analysis of Cancer Data (A)

of cancer data, including one or more of the following topics: Random Forest Algorithms § § Fuzzy-Set Analysis § § Non-Linear Signal Processing § § Bootstrapping Methods § § Imputation Algorithms § § Bayesian Classifiers § § Support Vector Machines § § Time-to-Event Models § § K-Means Cluster Analysis § § Discriminant Analysis Classifiers § § K-Nearest Neighbor Methods § § Multiple Comparison Strategies § § Cancer Influence Modelling § § Hyperplane Bernoulli Ring Sets § § Network Analysis § § Target Prediction and Cross-Validation Algorithms § § Hybrid and Hierarchical Partitioning § § Self-Organizing Maps § § Causal Validity and Benchmarking § § Cancer Informatics represents a hybrid discipline encompassing the fields of oncology, computer science, bioinformatics, statistics, computational biology, genomics, proteomics, metabolomics, pharmacology, and quantitative epidemiology. The common bond or challenge that unifies the various disciplines is the need to bring order to the massive amounts of data generated by researchers and clinicians attempting to find the underlying causes and effective means of diagnosing and treating cancer.