Supervised selective kernel fusion for membrane protein prediction

Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combining different data sources via supervised selective kernel fusion was proposed in our previous papers. It includes, as particular cases, SVM, Lasso SVM, Elastic Net SVM and others. In this paper we apply a further instantiation of this approach, the Supervised Selective Support Kernel SVM and demonstrate that the proposed approach achieves the top-rank position among the selective kernel fusion variants on benchmark data for membrane protein prediction. The method differs from the previous approaches in that it naturally derives a subset of “support kernels” (analogous to support objects within SVMs), thereby allowing the memory-efficient exclusion of significant numbers of irrelevant kernel matrixes from a decision rule in a manner particularly suited to membrane protein prediction

Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution

In multimodal biometric information fusion, it is common to encounter missing modalities in which matching cannot be performed. As a result, at the match score level, this implies that scores will be missing. We address the multimodal fusion problem involving missing modalities (scores) using support vector machines with the Neutral Point Substitution (NPS) method. The approach starts by processing each modality using a kernel. When a modality is missing, at the kernel level, the missing modality is substituted by one that is unbiased with regards to the classification, called a neutral point. Critically, unlike conventional missing-data substitution methods, explicit calculation of neutral points may be omitted by virtue of their implicit incorporation within the SVM training framework. Experiments based on the publicly available Biosecure DS2 multimodal (scores) data set shows that the SVM-NPS approach achieves very good generalization performance compared to the sum rule fusion, especially with severe missing modalities

A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets

It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a blank in the modality-specific kernel matrix at the relevant position. We propose to fill the blank positions in the collection of training kernel matrices via a variant of the Neutral Point Substitution (NPS) method, where the term ”neutral point” stands for the locus of points defined by the ”neutral hyperplane” in the hypothetical linear space produced by the respective kernel. The current method crucially differs from the previously developed neutral point approach in that it is capable of treating missing data in the training set on the same basis as missing data in the test set. It is therefore of potentially much wider applicability. We evaluate the method on the Biosecure DS2 data set

Supervised selective kernel fusion for membrane protein prediction

Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combining different data sources via supervised selective kernel fusion was proposed in our previous papers. It includes, as particular cases, SVM, Lasso SVM, Elastic Net SVM and others. In this paper we apply a further instantiation of this approach, the Supervised Selective Support Kernel SVM and demonstrate that the proposed approach achieves the top-rank position among the selective kernel fusion variants on benchmark data for membrane protein prediction. The method differs from the previous approaches in that it naturally derives a subset of “support kernels” (analogous to support objects within SVMs), thereby allowing the memory-efficient exclusion of significant numbers of irrelevant kernel matrixes from a decision rule in a manner particularly suited to membrane protein prediction

Normoalbuminuric Diabetic Kidney Disease in the U.S. Population

This study sought to compare the prevalence and modifying factors of normoalbuminuric (NA) versus albuminuric (ALB) CKD in the U.S. diabetic and nondiabetic populations

Long-term effects of intensive glycemic and blood pressure control and fenofibrate use on kidney outcomes

Background and objectives In people with type 2 diabetes, aggressive control of glycemia, BP, and lipids have resulted in conflicting short-term (<5 years) kidney outcomes. We aimed to determine the long-term kidney effects of these interventions. Design, setting, participants, & measurements The Action to Control Cardiovascular Risk in Diabetes (ACCORD) was a multifactorial intervention study in people with type 2 diabetes at high risk for cardiovascular disease (n=10,251), to examine the effects of intensive glycemic control (hemoglobin A1c <6.0% versus 7%-7.9%), BP control (systolic BP <120 mm Hg versus <140 mm Hg) or fenofibrate versus placebo added to simvastatin on cardiovascular events and death. The glycemia trial lasted 3.7 years and participants were followed for another 6.5 years in ACCORDION, the ACCORD Follow-On Study. The post hoc primary composite kidney outcome was defined as incident macroalbuminuria, creatinine doubling, need for dialysis, or death by any cause. Cox proportional hazards regression estimated the effect of each intervention on the composite outcome and individual components. In secondary outcome analyses, competing risk regression was used to account for the risk of death in incident kidney outcomes. Analyses were adjusted for sociodemographics, randomization groups, and clinical factors. Results There were 988 cases of incident macroalbuminuria, 954 with doubling of creatinine, 351 requiring dialysis, and 1905 deaths. Hazard ratios (HRs) for the composite outcome with intensive glycemic, BP control, and fenofibrate use compared with standard therapy were 0.92 (95% confidence interval [95% CI], 0.86 to 0.98), 1.16 (95% CI, 1.05 to 1.28), and 1.16 (95% CI, 1.06 to 1.27). Multivariable, secondary outcome analyses showed that in the glycemia trial, only macroalbuminuria was significantly decreased (HR, 0.68; 95% CI, 0.59 to 0.77). In the BP and lipid trials, only creatinine doubling was affected (HR, 1.64; 95% CI, 1.30 to 2.06 and HR, 2.00; 95% CI, 1.61 to 2.49, respectively). Conclusions In people with type 2 diabetes at high risk for cardiovascular disease, intensive glycemic control may result in a long-term reduction in macroalbuminuria; however, intensive BP control and fenofibrates may increase the risk for adverse kidney events

Isolated communities of Epsilonproteobacteria in hydrothermal vent fluids of the Mariana Arc seamounts

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in FEMS Microbiology Ecology 73 (2010): 538-549, doi:10.1111/j.1574-6941.2010.00910.x.Low-temperature hydrothermal vent fluids represent access points to diverse microbial communities living in oceanic crust. This study examined the distribution, relative abundance, and diversity of Epsilonproteobacteria in 14 low-temperature vent fluids from 5 volcanically active seamounts of the Mariana Arc using a 454 tag sequencing approach. Most vent fluids were enriched in cell concentrations compared to background seawater, and quantitative PCR results indicated all fluids were dominated by bacteria. Operational taxonomic unit (OTU)-based statistical tools applied to 454 data show that all vents from the northern end of the Marian Arc grouped together, to the exclusion of southern arc seamounts, which were as distinct from one another as they were from northern seamounts. Statistical analysis also showed a significant relationship between seamount and individual vent groupings, suggesting that community membership may be linked to geographical isolation and not geochemical parameters. However, while there may be large-scale geographic differences, distance is not the distinguishing factor in microbial community composition. At the local scale, most vents host a distinct population of Epsilonprotoebacteria, regardless of seamount location. This suggests there may be barriers to exchange and dispersal for these vent endemic microorganisms at hydrothermal seamounts of the Mariana Arc.This work was supported by a National Research Council Research Associateship Award and L’Oréal USA Fellowship (J.A.H.), NASA Astrobiology Institute Cooperative Agreement NNA04CC04A (M.L.S.), the Alfred P. Sloan Foundation’s ICoMM field project, and the W. M. Keck Foundation. This publication is [partially] funded by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement No. NA17RJ1232, Contribution #1814

Genetic influence on variation in serum uric acid in American Indians: the strong heart family study

Hyperuricemia is associated with the metabolic syndrome, gout, renal and cardiovascular disease (CVD). American Indians have high rates of CVD and 25 % of individuals in the Strong Heart Family Study (SHFS) have high serum uric acid levels. The aim of this study was to investigate the genetic determinants of serum uric acid variation in American Indian participants of the SHFS. A variance component decomposition approach (implemented in SOLAR) was used to conduct univariate genetic analyses in each of three study centers and the combined sample. Serum uric acid was adjusted for age, sex, age*sex, BMI, estimated glomerular filtration rate, alcohol intake, diabetic status and medications. Overall mean ± SD serum uric acid for all individuals was 5.14 ± 1.5 mg/dl. Serum uric acid was found to be significantly heritable (0.46 ± 0.03 in all centers, and 0.39 ± 0.07, 0.51 ± 0.05, 0.44 ± 0.06 in Arizona, Dakotas and Oklahoma, respectively). Multipoint linkage analysis showed significant evidence of linkage for serum uric acid on chromosome 11 in the Dakotas center (logarithm of odds score (LOD) = 3.02) and in the combined sample (LOD = 3.56) and on chromosome 1 (LOD = 3.51) in the combined sample. A strong positional candidate gene in the chromosome 11 region is solute carrier family22, member 12 (SLC22A12) that encodes a major uric acid transporter URAT1. These results show a significant genetic influence and a possible role for one or more genes on chromosomes 1 and 11 on the variation in serum uric acid in American Indian populations

Metatranscriptomics reveal differences in in situ energy and nitrogen metabolism among hydrothermal vent snail symbionts

Despite the ubiquity of chemoautotrophic symbioses at hydrothermal vents, our understanding of the influence of environmental chemistry on symbiont metabolism is limited. Transcriptomic analyses are useful for linking physiological poise to environmental conditions, but recovering samples from the deep sea is challenging, as the long recovery times can change expression profiles before preservation. Here, we present a novel, in situ RNA sampling and preservation device, which we used to compare the symbiont metatranscriptomes associated with Alviniconcha, a genus of vent snail, in which specific host–symbiont combinations are predictably distributed across a regional geochemical gradient. Metatranscriptomes of these symbionts reveal key differences in energy and nitrogen metabolism relating to both environmental chemistry (that is, the relative expression of genes) and symbiont phylogeny (that is, the specific pathways employed). Unexpectedly, dramatic differences in expression of transposases and flagellar genes suggest that different symbiont types may also have distinct life histories. These data further our understanding of these symbionts' metabolic capabilities and their expression in situ, and suggest an important role for symbionts in mediating their hosts' interaction with regional-scale differences in geochemistry