Information-Theoretic Inference of an Optimal Dictionary of Protein Supersecondary Structures View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019-04-04

AUTHORS

Arun S. Konagurthu , Ramanan Subramanian , Lloyd Allison , David Abramson , Maria Garcia de la Banda , Peter J. Stuckey , Arthur M. Lesk

ABSTRACT

We recently developed an unsupervised Bayesian inference methodology to automatically infer a dictionary of protein supersecondary structures (Subramanian et al., IEEE data compression conference proceedings (DCC), 340–349, 2017). Specifically, this methodology uses the information-theoretic framework of minimum message length (MML) criterion for hypothesis selection (Wallace, Statistical and inductive inference by minimum message length, Springer Science & Business Media, New York, 2005). The best dictionary of supersecondary structures is the one that yields the most (lossless) compression on the source collection of folding patterns represented as tableaux (matrix representations that capture the essence of protein folding patterns (Lesk, J Mol Graph. 13:159–164, 1995). This book chapter outlines our MML methodology for inferring the supersecondary structure dictionary. The inferred dictionary is available at http://lcb.infotech.monash.edu.au/proteinConcepts/scop100/dictionary.html. More... »

PAGES

123-131

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4939-9161-7_6

DOI

http://dx.doi.org/10.1007/978-1-4939-9161-7_6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113182586

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30945216


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