Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures

Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures

One of the gravest dangers facing cancer patients is an extended symptom-free lull between tumor initiation and the first diagnosis. Detection of tumors is critical for effective intervention. Using the body's immune system to detect and amplify tumor-specific signals may enable detection of cancer using an inexpensive immunoassay. Immunosignatures are one such assay: they provide a map of anti...

Application of Independent Component Analysis to Microarray Data

Independent Component Analysis (ICA) is introduced from the viewpoint of maximal information transfer for single neurons. This historical motivation for the development of ICA may be interesting from the viewpoint of independent agents because each neuron can be seen as a single agent. The current article compares the performance of ICA with Principal Component Analysis (PCA) for detecting core...

Outcome-Driven Cluster Analysis with Application to Microarray Data

One goal of cluster analysis is to sort characteristics into groups (clusters) so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additio...

cDNA Microarray Data Normalization

Bayesian Pathway Analysis of Cancer Microarray Data

High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTB...