Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

A new instrument for targeting falls prevention interventions was accurate and clinically applicable in a hospital setting

Background and Objective: To describe the diagnostic accuracy and practical application of the Peter James Centre Falls Risk Assessment Tool (PJC-FRAT), a multidisciplinary falls risk screening and intervention deployment instrument. Methods: In phase 1, the accuracy of the PJC-FRAT was prospectively compared to a gold standard (the STRATIFY) on a cohort of subacute hospital patients (n = 122). In phase 2, the PJC-FRAT was temporally reassessed using a subsequent cohort (n = 316), with results compared to those of phase 1. Primary outcomes were falls (events), fallers (patients who fell), and hospital completion rates of the PJC-FRAT. Results: In phase 1, PJC-FRAT accuracy of identifying falters showed sensitivity of 73% (bootstrap 95% confidence interval CI = 55, 90) and specificity of 75% (95% CI = 66, 83), compared with the STRATIFY (cutoff >= 2/5) sensitivity of 77% (95% CI = 59, 92) and specificity of 51% (95% CI = 41, 61). This difference was not significant. In phase 2, accuracy of nursing staff using the PJC-FRAT was lower. PJC-FRAT completion rates varied among disciplines over both phases: nurses and physiotherapists, >= 90%; occupational therapists, >= 82%; and medical officers, >= 57%. Conclusion: The PJC-FRAT was practical and relatively accurate as a predictor of falls and a deployment instrument for falls prevention interventions, although continued staff education may be necessary to maintain its accuracy. (c) 2006 Elsevier Inc. All rights reserved.