Biological Data Mining by Jake Y. Chen, Stefano Lonardi

By Jake Y. Chen, Stefano Lonardi

Like a data-guzzling rapid engine, complicated information mining has been powering post-genome organic reports for 2 many years. Reflecting this progress, organic facts Mining provides complete facts mining techniques, theories, and functions in present organic and scientific learn. each one bankruptcy is written by way of a unique crew of interdisciplinary information mining researchers who disguise cutting-edge organic topics.

The first component to the e-book discusses demanding situations and possibilities in studying and mining organic sequences and buildings to realize perception into molecular capabilities. the second one part addresses rising computational demanding situations in analyzing high-throughput Omics info. The publication then describes the relationships among facts mining and similar components of computing, together with wisdom illustration, info retrieval, and knowledge integration for established and unstructured organic facts. The final half explores rising facts mining possibilities for biomedical applications.

This quantity examines the ideas, difficulties, development, and traits in constructing and utilising new information mining strategies to the speedily becoming box of genome biology. through learning the suggestions and case stories awarded, readers will achieve major perception and enhance useful options for comparable organic information mining initiatives sooner or later.

Show description

Read Online or Download Biological Data Mining PDF

Best data mining books

Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry: Third International Conference, MDA

This e-book constitutes the refereed court cases of the overseas convention on Mass info research of pictures and signs in medication, Biotechnology, Chemistry and nutrition undefined, MDA 2008, held in Leipzig, Germany, on July 14, 2008. The 18 complete papers awarded have been conscientiously reviewed and chosen for inclusion within the publication.

Applied Data Mining : Statistical Methods for Business and Industry (Statistics in Practice)

Information mining could be outlined because the strategy of choice, exploration and modelling of huge databases, on the way to become aware of types and styles. The expanding availability of information within the present details society has ended in the necessity for legitimate instruments for its modelling and research. facts mining and utilized statistical tools are the correct instruments to extract such wisdom from facts.

Dark Web: Exploring and Data Mining the Dark Side of the Web

The collage of Arizona man made Intelligence Lab (AI Lab) darkish internet venture is a long term medical study application that goals to check and comprehend the foreign terrorism (Jihadist) phenomena through a computational, data-centric technique. We objective to assemble "ALL" websites generated through foreign terrorist teams, together with websites, boards, chat rooms, blogs, social networking websites, video clips, digital global, and so forth.

Beginning Apache Pig: Big Data Processing Made Easy

Discover ways to use Apache Pig to strengthen light-weight vast info purposes simply and speedy. This ebook indicates you several optimization strategies and covers each context the place Pig is utilized in sizeable facts analytics. starting Apache Pig exhibits you the way Pig is simple to profit and calls for quite little time to advance huge info functions.

Extra resources for Biological Data Mining

Example text

2006. Consensus folding of unaligned RNA sequences revisited. J. Comput. Biol. 13:283–295. D. 2001. Discovering common stem-loop motifs in unaligned RNA sequences. Nucleic Acids Res. 29:2135–2144. H. 2002. Dynalign: an algorithm for finding the secondary structure common to two RNA sequences. J. Mol. Biol. 317:191–203. M. 2002. Pairwise RNA structure comparison with stochastic context-free grammars. In Proceedings of the Pacific Symposium Biocomputing, Lihue, Hawaii, 163–174. F. 2004. Alignment of RNA base pairing probability matrices.

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. Superimpose the query protein with each candidate protein and compute the root mean square deviation (RMSD). Step 1 selects from the approximately 47,000 proteins of the PDB a list of candidates typically of size less than 1000.

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 . . . . . . . . . 3 Ranking candidate proteins . . . . . . . . . . . . . . . . . . 4 Atomic superposition .

Download PDF sample

Rated 4.12 of 5 – based on 15 votes