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The main aim of this core is to incorporate aspects of high-throughput and high-performance computing with knowledge discovery approaches through the application of neural-networks, probability and statistics to support and enhance each of the participating projects and the Molecular Analysis Core (Core B). In short, our goal is to facilitate the multi-step data-intensive nature of the knowledge discovery process (Fig. 1) utilizing the latest tools and techniques as applied to the COBRE-related projects.
We propose the following three aims to lead the Bioinformatics Core activities:
Aim 1: Service: create a structure to support COBRE investigators
Aim 2: Research and Development: develop new techniques and algorithms that supplement the currently available off-the-shelf and open-source packages
Aim 3: Education: introduce and train young and established investigators in available bioinformatics technologies
Figure 1. The knowledge discovery pipeline. Typical microarray experiment begins with a goodexperimental design. Poor quality data are filtered out. The resulting data are analyzed (tailored to study aims) by inferring statistical significance of differential expression, exploratory data analyses, classification of samples according to disease subtypes, and then biologically validated after ontological, pathway or other analysis. The data at each step is collected according to an industry standard, e.g. the MIAME (Minimum Information About a Microarray Experiment) standard for microarray data, and archived properly.
The project described was supported by NIH Grant Number
P20RR018724 from the National Center for Research Resources.