Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Interaction among nitric oxide (NO)-related genes in migraine susceptibility

The pathogenic mechanisms involved in migraine are complex and not completely clarified. Because there is evidence for the involvement of nitric oxide (NO) in migraine pathophysiology, candidate gene approaches focusing on genes affecting the endothelial function have been studied including the genes encoding endothelial NO synthase (eNOS), inducible NO synthase (iNOS), and vascular endothelial growth factor (VEGF). However, investigations on gene-gene interactions are warranted to better elucidate the genetic basis of migraine. This study aimed at characterizing interactions among nine clinically relevant polymorphisms in eNOS (T-786C/rs2070744, the 27 bp VNTR in intron 4, the Glu298Asp/rs1799983, and two additional tagSNPs rs3918226 and rs743506), iNOS (C(-1026)A/rs2779249 and G2087A/rs2297518), and VEGF (C(-2578)A/rs699947 and G(-634)C/rs2010963) in migraine patients and control group. Genotypes were determined by real-time polymerase chain reaction using the Taqman(A (R)) allele discrimination assays or PCR and fragment separation by electrophoresis in 99 healthy women without migraine (control group) and in 150 women with migraine divided into two groups: 107 with migraine without aura and 43 with aura. The multifactor dimensionality reduction method was used to detect and characterize gene-gene interactions. We found a significant interaction between eNOS rs743506 and iNOS 2087G/A polymorphisms in migraine patients compared to control group (P < 0.05), suggesting that this combination affect the susceptibility to migraine. Further studies are needed to determine the molecular mechanisms explaining this interaction.

Epistasis among eNOS, MMP-9 and VEGF maternal genotypes in hypertensive disorders of pregnancy

Polymorphisms of the endothelial nitric oxide synthase (eNOS), matrix metalloproteinase-9 (MMP-9) and vascular endothelial growth factor (VEGF) genes were shown to be associated with hypertensive disorders of pregnancy. However, epistasis is suggested to be an important component of the genetic susceptibility to preeclampsia (PE). The aim of this study was to characterize the interactions among these genes in PE and gestational hypertension (GH). Seven clinically relevant polymorphisms of eNOS (T-786C, rs2070744, a variable number of tandem repeats in intron 4 and Glu298Asp, rs1799983), MMP-9 (C-1562T, rs3918242 and -90(CA)(13-25), rs2234681) and VEGF (C-2578A, rs699947 and G-634C, rs2010963) were genotyped by TaqMan allelic discrimination assays or PCR and fragment separation by electrophoresis in 122 patients with PE, 107 patients with GH and a control group of 102 normotensive pregnant (NP) women. A robust multifactor dimensionality reduction analysis was used to characterize gene-gene interactions. Although no significant genotype combinations were observed for the comparison between the GH and NP groups (P>0.05), the combination of MMP-9-1562CC with VEGF-634GG was more frequent in NP women than in women with PE (P<0.05). Moreover, the combination of MMP-9-1562CC with VEGF-634CC or MMP-9-1562CT with VEGF-634CC or-634GG was more frequent in women with PE than in NP women (P<0.05). These results are obscured when single polymorphisms in these genes are considered and suggest that specific genotype combinations of MMP-9 and VEGF contribute to PE susceptibility. Hypertension Research (2012) 35, 917-921; doi:10.1038/hr.2012.60; published online 10 May 2012