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This principle could form the basis of a mining strategy for accelerating discovery of function in genome sequences.
The general mining strategy for biomarker discovery reported here is flexible in nature.
The data mining strategy for the discovery of cancer biomarkers is based on our recently published methodology exploring the cancer microarray platform, Oncomine, and employing the advanced knowledge bases of Ingenuity Systems, Ingenuity Pathways Analysis, to identify extracellular hydrolases in various types of cancer (unpublished results).
For instance, we have developed a data mining strategy for the specific purpose of analyzing blood transcriptional profiles [ 15].
We conclude that the combined in silico data analysis is an effective data mining strategy for the identification of tissue-specific gene expression.
Cornidia an excellent study case for testing the performance of potential NSCG retrieved by a genome scale mining strategy for resolving fine scale phylogenetic relationships.
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More recently, different machine learning and data mining strategies for feature selection have been applied to identifying a subset of genes that can maximize the prediction performance [ 19].
For epidemiologic studies and clinical applications, proteomic mining strategies for biomarker discovery have focused on blood serum and plasma, because this compartment is relatively accessible and potentially provides a host of diagnostic information (Lathrop et al. 2003).
Thus, metagenomics has been heralded as a promising mining strategy of resources for the biotechnological and pharmaceutical industry.
Hence, we proposed an iteratively mining strategy, family-based mining, for collecting a set of satisfied W-patterns that together serve as the diagnostic W-patterns for the input sequences.
The herein developed and demonstrated transcriptome based mining strategy has a great potential for the design of novel and highly informative nuclear markers for a range of plant groups and evolutionary scales.
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