[Meta-analysis on the relationship between leptin receptor Gln223Arg and Pro1019Pro gene polymorphism and obesity in the Chinese population].

OBJECTIVE: To evaluate the associations between polymorphisms of LEPR Gln223Arg, LEPR Pro1019Pro and the risk on obesity. METHODS: A computerized search on literature was carried out in Wanfang, CNKI, VIP databases and CBM, PubMed, EMBASE databases to collect articles published between 1979 and 2010 concerning the associations between polymorphisms of LEPR Gln223Arg and/or LEPR Pro1019Pro and risk of obesity in the Chinese population. Odds ratios (ORs) were used to assess the strength of the association, and 95% confidence intervals (CIs) to present the precision of the estimates. Meta-analysis was performed using the STATA statistical software. RESULTS: Fifteen literature were collected for Meta-analysis by the uniform inclusion and exclusion criteria. There were 1096 obese patients and 949 controls for polymorphisms of LEPR Gln223Arg in 9 papers, together with 961 obese patients and 818 controls for polymorphisms of LEPR Pro1019Pro in 8 papers. Overall, there were significant associations between decreased risk of obesity and LEPR Gln223Arg polymorphisms (-668 A→G) (G versus A, OR = 0.66, 95%CI: 0.49 - 0.89; AG and GG versus AA, OR = 0.50, 95%CI: 0.32 - 0.77; respectively). There were significant associations between increased risk of obesity and LEPR Pro1019Pro polymorphisms (-3057 G→A) (A versus G, OR = 1.61, 95%CI: 1.15 - 2.26; AG and AA versus GG, OR = 1.50, 95%CI: 1.08 - 2.08; respectively). CONCLUSION: Variant alleles at both LEPR-668 and LEPR-3057 were associated with obesity in the Chinese Han-dominated population.

Splicing-site recognition of rice (Oryza sativa L.) DNA sequences by support vector machines.

MOTIVATION: It was found that high accuracy splicing-site recognition of rice (Oryza sativa L.) DNA sequence is especially difficult. We described a new method for the splicing-site recognition of rice DNA sequences. METHOD: Based on the intron in eukaryotic organisms conforming to the principle of GT-AG, we used support vector machines (SVM) to predict the splicing sites. By machine learning, we built a model and used it to test the effect of the test data set of true and pseudo splicing sites. RESULTS: The prediction accuracy we obtained was 87.53% at the true 5' end splicing site and 87.37% at the true 3' end splicing sites. The results suggested that the SVM approach could achieve higher accuracy than the previous approaches.