Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

Genome-Wide Identification, Characterization and Phylogenetic Analysis of the Rice LRR-Kinases

LRR-kinases constitute the largest subfamily of receptor-like kinases in plants and regulate a wide variety of processes related to development and defense. Through a reiterative process of sequence analysis and re-annotation, we identified 309 LRR-kinase genes in the rice genome (Nipponbare). Among them, 127 genes in the Rice Annotation Project Database and 85 in Refseq of NCBI were amended (in addition, 62 LRR-kinase genes were not annotated in Refseq). The complete set of LRR-kinases was characterized. These LRR-kinases were classified into five groups according to phylogenetic analysis, and the genes in groups 1, 2, 3 and 4 usually have fewer introns than those in group 5. The introns in the LRR domain, which are highly conserved in regards to their positions and configurations, split the first Leu or other amino residues at this position of the ‘xxLxLxx’ motif with phase 2 and usually separate one or more LRR repeats exactly. Tandemly repeated LRR motifs have evolved from exon duplication, mutation and exon shuffling. The extensive distribution and diversity of the LRR-kinase genes have been mainly generated by tandem duplication and mutation after whole genome duplication. Positive selection has made a limited contribution to the sequence diversity after duplication, but positively selected sites located in the LRR domain are thought to involve in the protein-protein interaction