Download Biological Data Mining (Chapman & Hall Crc Data Mining and by Jake Y. Chen, Stefano Lonardi PDF

By Jake Y. Chen, Stefano Lonardi

Like a data-guzzling rapid engine, complicated info mining has been powering post-genome organic experiences for 2 many years. Reflecting this progress, organic information Mining offers finished info mining ideas, theories, and purposes in present organic and clinical examine. every one bankruptcy is written by means of a wonderful group of interdisciplinary facts mining researchers who disguise state of the art organic themes. the 1st part of the publication discusses demanding situations and possibilities in reading and mining organic sequences and buildings to realize perception into molecular services. the second one part addresses rising computational demanding situations in analyzing high-throughput Omics facts. The publication then describes the relationships among facts mining and similar parts of computing, together with wisdom illustration, info retrieval, and knowledge integration for established and unstructured organic facts. The final half explores rising info mining possibilities for biomedical functions. This quantity examines the strategies, difficulties, growth, and developments in constructing and employing new information mining concepts to the quickly turning out to be box of genome biology. via learning the thoughts and case stories provided, readers will achieve major perception and enhance sensible ideas for related organic info mining tasks sooner or later.

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Additional resources for Biological Data Mining (Chapman & Hall Crc Data Mining and Knowledge Discovery Series)

Sample text

3 The Use of Geometric Invariants for Three-Dimensional (3D) Structures Comparison . . . . . . . . . . . . . . . . . . . . . . . . 1 Retrieving similarity from the table . . . . . . . . . . . . . . 2 Pair-wise alignment of secondary structures . . . . . . . . . 3 Ranking candidate proteins . . . . . . . . . . . . . . . . . . 4 Atomic superposition . . . . . . . . . . . . . . . .

The first protein with a different fold appears at position 607. As another example, we have considered as a query protein the triose phosphate isomerase from chicken muscle (PDB:ID 1TIM); more than 400 chains are correctly recognized before a protein with a different fold (according to the SCOP classification) is found. To assess the quality of our results we use the measure of accuracy defined as the percentage of correctly classified proteins in the top n items outputs, for various values of n. An output is considered correct, or true positive, if the protein is classified by SCOP in the same fold as the query protein.

5 Benchmark applications . . . . . . . . . . . . . . . . . . . . 4 Statistical Analysis of Triplets and Quartets of Secondary Structure Element (SSE) . . . . . . . . . . . . . . . . . . . . . . . 1 Methodology for the analysis of angular patterns . . . . . . . 2 Results of the statistical analysis . . . . . . . . . . . . . . . 3 Selection of subsets containing secondary structure element (SSE) in close contact . . . . . . .

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