Biological Data Mining by Jake Y. Chen, Stefano Lonardi
By Jake Y. Chen, Stefano Lonardi
Like a data-guzzling faster engine, complex facts mining has been powering post-genome organic experiences for 2 many years. Reflecting this progress, organic facts Mining provides entire information mining ideas, theories, and purposes in present organic and clinical examine. each one bankruptcy is written via a individual staff of interdisciplinary facts mining researchers who conceal state of the art organic topics.
The first element of the publication discusses demanding situations and possibilities in reading and mining organic sequences and buildings to achieve perception into molecular services. the second one part addresses rising computational demanding situations in studying high-throughput Omics information. The ebook then describes the relationships among info mining and comparable parts of computing, together with wisdom illustration, details retrieval, and knowledge integration for established and unstructured organic information. The final half explores rising information mining possibilities for biomedical applications.
This quantity examines the strategies, difficulties, development, and tendencies in constructing and utilizing new information mining recommendations to the swiftly becoming box of genome biology. through learning the recommendations and case stories provided, readers will achieve major perception and enhance sensible ideas for comparable organic facts mining initiatives sooner or later.
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Additional resources for Biological Data Mining
The task is to align the SSEs of the two structures by ﬁnding the longest sequence of well matched pairs. A solution to the alignment problem is obtained by the dynamic programming technique. The scoring function used in the algorithm compares pairs of segments from each of the two proteins; the algorithm maximizes a score that represents the degree of similarity between these segments. , for all pairs in A, if (pi , qj ) and (ph , qk ) are two aligned pairs of A and i < h, then it must also be j < k.
2002. Secondary structure prediction for aligned RNA sequences. J. Mol. Biol. 319:1059–1066. F. 2008. RNAalifold: improved consensus structure prediction for RNA alignments. BMC Bioinformatics 9:474. R. 2003. RSEARCH: ﬁnding homologs of single structured RNA sequences. BMC Bioinformatics 4:44. , Hein, J. 2003. Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Res. 31:3423–3428. D. 1995. Graph-theoretic approach to RNA modeling using comparative data.
1 The dilemma of protein folding . . . . . . . . . . . . . . . . 2 Protein classiﬁcation and the discovery of hidden rules . . . . 2 The Use of Geometric Invariants and Hashing for a Simpliﬁed Representation of Secondary Structure Elements (SSEs) . . . . . . 1 Simpliﬁed representations of three-dimensional (3D) structures . . . . . . . . . . . . . . . . . . . . . . . . 2 Segment approximation of secondary structure element (SSE) . . .