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Multiple Classifiers (multiple + classifier)

Distribution by Scientific Domains
Chemistry100%

Selected Abstracts

Cascaded multiple classifiers for secondary structure prediction

PROTEIN SCIENCE, Issue 6 2000
Mohammed Ouali
Abstract We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new nonredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barton and JA. Cuff). This database was especially designed to train and test protein secondary structure prediction methods, and it uses a more stringent definition of homologous sequence than in previous studies. We show that it is possible to design classifiers that can highly discriminate the three classes (H, E, C) with an accuracy of up to 78% for ,-strands, using only a local window and resampling techniques. This indicates that the importance of long-range interactions for the prediction of ,-strands has been probably previously overestimated. [source]

Improved classification of crystallization images using data fusion and multiple classifiers

ACTA CRYSTALLOGRAPHICA SECTION D, Issue 8 2008
Samarasena Buchala
Identifying the conditions that will produce diffraction-quality crystals can require very many crystallization experiments. The use of robots has increased the number of experiments performed in most laboratories, while in structural genomics centres tens of thousands of experiments can be produced every day. Reliable automated evaluation of these experiments is becoming increasingly important. A more robust classification is achieved by combining different methods of feature extraction with the use of multiple classifiers. [source]

Integrated state evaluation for the images of crystallization droplets utilizing linear and nonlinear classifiers

ACTA CRYSTALLOGRAPHICA SECTION D, Issue 9 2006
Kuniaki Kawabata
In a usual crystallization process, the researchers evaluate the protein crystallization growth states based on visual impressions and repeatedly assign scores throughout the growth process. Although the development of crystallization robotic systems has generally realised the automation of the setup and storage of crystallization samples, evaluation of crystallization states has not yet been completely automated. The method presented here attempts to categorize individual crystallization droplet images into five classes using multiple classifiers. In particular, linear and nonlinear classifiers are utilized. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process using linear discriminant analysis (LDA) and support vector machine (SVM). The performance of this method has been evaluated by comparing the results obtained using the method with the results obtained by a human expert and the concordance rate was 84.4%. [source]