A generic optimising feature extraction method using multiobjective genetic programming

In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem. (C) 2010 Elsevier B.V. All rights reserved

Nocardia kroppenstedtii sp. nov., a novel actinomycete isolated from a lung transplant patient with a pulmonary infection

An actinomycete, strain N1286T, isolated from a lung transplant patient with a pulmonary infection, was provisionally assigned to the genus Nocardia. The strain had chemotaxonomic and morphological properties typical of members of the genus Nocardia and formed a distinct phyletic line in the Nocardia 16S rRNA gene tree. It was most closely related to Nocardia farcinica DSM 43665T (99.8% gene similarity) but was distinguished from the latter by a low level of DNA:DNA relatedness. These strains were also distinguished by a broad range of phenotypic properties. On the basis of these data, it is proposed that isolate N1286T (=DSM 45810T = NCTC 13617T) should be classified as the type strain of a new Nocardia species for which the name Nocardia kroppenstedtii is proposed

Automated Protein Structure Classification: A Survey

Classification of proteins based on their structure provides a valuable resource for studying protein structure, function and evolutionary relationships. With the rapidly increasing number of known protein structures, manual and semi-automatic classification is becoming ever more difficult and prohibitively slow. Therefore, there is a growing need for automated, accurate and efficient classification methods to generate classification databases or increase the speed and accuracy of semi-automatic techniques. Recognizing this need, several automated classification methods have been developed. In this survey, we overview recent developments in this area. We classify different methods based on their characteristics and compare their methodology, accuracy and efficiency. We then present a few open problems and explain future directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront