873 resultados para Support Vector Machines and Naive Bayes Classifier


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Mode of access: Internet.

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Supporting the forearm on the work surface during keyboard operation may increase comfort, decrease muscular load of the neck and shoulders, and decrease the time spent in ulnar deviation. Wrist rests are used widely in the workplace and are more commonly being incorporated in keyboard design. The aim of this study was to examine the effect of wrist rest use on wrist posture during forearm Support. A laboratory based, experimental study was conducted (subjects n = 15) to examine muscle activity and wrist Postures during keyboard and mouse tasks in each of' two conditions; wrist rest and no wrist rest. There were no significant differences for right wrist flexion/extension between use of a wrist rest and no wrist rest for keyboard or mouse use. Left wrist extension was significantly higher without a wrist rest than with a wrist rest during keyboard use (df = 14; t = 2.95; p = 0.01; d = 0.38). No differences with respect to use of a wrist rest were found for the left or right hand for ulnar deviation For keyboard or mouse use. There were no differences in muscle activity between the test conditions for keyboard use. Relevance to industry Wrist rests are used widely in the workplace and are more commonly being incorporated in keyboard design. Use of a wrist rest in conjunction with forearm support when using a conventional desk does not appear to have any impact on wrist posture or muscle activity during keyboard use. (C) 2004 Elsevier B.V. All rights reserved.

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Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.

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In this study, we propose a novel method to predict the solvent accessible surface areas of transmembrane residues. For both transmembrane alpha-helix and beta-barrel residues, the correlation coefficients between the predicted and observed accessible surface areas are around 0.65. On the basis of predicted accessible surface areas, residues exposed to the lipid environment or buried inside a protein can be identified by using certain cutoff thresholds. We have extensively examined our approach based on different definitions of accessible surface areas and a variety of sets of control parameters. Given that experimentally determining the structures of membrane proteins is very difficult and membrane proteins are actually abundant in nature, our approach is useful for theoretically modeling membrane protein tertiary structures, particularly for modeling the assembly of transmembrane domains. This approach can be used to annotate the membrane proteins in proteomes to provide extra structural and functional information.