By Jake Y. Chen, Stefano Lonardi
Like a data-guzzling faster engine, complicated information mining has been powering post-genome organic reports for 2 many years. Reflecting this development, organic info Mining offers accomplished facts mining innovations, theories, and functions in present organic and scientific examine. each one bankruptcy is written via a exceptional workforce of interdisciplinary info mining researchers who conceal cutting-edge organic topics.
The first part of the ebook discusses demanding situations and possibilities in examining and mining organic sequences and buildings to achieve perception into molecular services. the second one part addresses rising computational demanding situations in analyzing high-throughput Omics facts. The ebook then describes the relationships among information mining and similar parts of computing, together with wisdom illustration, details retrieval, and knowledge integration for established and unstructured organic info. The final half explores rising info mining possibilities for biomedical applications.
This quantity examines the innovations, difficulties, development, and tendencies in constructing and making use of new information mining recommendations to the speedily transforming into box of genome biology. via learning the techniques and case reports offered, readers will achieve major perception and strengthen useful options for related organic info mining initiatives sooner or later.
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Extra info for Biological Data Mining
Example text
3 Building of the hash table for triplets of secondary structure element (SSE) . . . . . . . . . . . . . . . . . . . . 4 Building the hash table . . . . . . . . . . . . . . . . . . . . 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 .
There are three main steps in our search, where each step refines the results of the previous step using a more computation-intensive procedure. Step 1. Access the hash table to find a list of proteins that are good candidates for similarity with the query. Step 2. For each candidate protein, perform a pair-wise structure alignment with the query protein. Rank the candidate proteins based on the score of the alignments and remove from the list the candidates with a score below a given threshold. Step 3.
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 . . . . . . . . . . . . . . . . . . . . . 5 Benchmark applications .