Computational Advances in Cancer Informatics (A)

Gene Set Enrichment Analysis § § Hybrid Computing § § Efficient Cloud Storage and Retrieval § § Matching of Expression Patterns § § Multi-Modal Analysis § § Splice Variations and Chip Seq System Algorithms § § Rapid High-Throughput Analysis § § Computational Molecular Profiling § § Digital Gene Expression Analysis § § De Novo Genome Assembly and Re-Sequencing § § Computational Methods Computational Drug Repurposing § § Accelerated Next Generation Sequencing Technologies § § and Search Engines Post-Transcriptional Pattern Recognition § § Computational Advances in High-Content Platform § § Analysis Data visualization Software Development § § Machine Learning and Integrative Computer § § Approaches for Cancer Omics Research 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 treating cancer.