Supervised inference of gene-regulatory networks

<p>Abstract</p> <p>Background</p> <p>Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks.</p> <p>Results</p> <p>The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed.</p> <p>Conclusion</p> <p>Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.</p

Filling Kinetic Gaps: Dynamic Modeling of Metabolism Where Detailed Kinetic Information Is Lacking

Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.This paper presents a statistic framework capable to study how and how fast the metabolites participating in a perturbed metabolic network reach a steady-state. Instead of requiring accurate kinetic information, this approach uses high throughput metabolome technology to define a feasible kinetic library, which constitutes the base for identifying, statistical and dynamical properties during the relaxation. For the sake of illustration we have applied this approach to the human Red blood cell metabolism (hRBC) and its capacity to predict temporal phenomena was evaluated. Remarkable, the main dynamical properties obtained from a detailed kinetic model in hRBC were recovered by our statistical approach. Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation.In this work we present an approach that integrates high throughput metabolome data to define the dynamic behavior of a slightly perturbed metabolic network where kinetic information is lacking. Having information of metabolite concentrations at steady-state, this method has significant relevance due its potential scope to analyze others genome scale metabolic reconstructions. Thus, I expect this approach will significantly contribute to explore the relationship between dynamic and physiology in other metabolic reconstructions, particularly those whose kinetic information is practically nulls. For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages

How to create an operational multi-model of seasonal forecasts?

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas.The research leading to these results is part of the Copernicus Climate Change Service (C3S) (Framework Agreement number C3S_51_Lot3_BSC), a program being implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. Francisco Doblas-Reyes acknowledges the support by the H2020 EUCP project (GA 776613) and the MINECO-funded CLINSA project (CGL2017-85791-R)

Physical activity, cardiorespiratory fitness, and metabolic syndrome in adolescents: A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>In adults, there is a substantial body of evidence that physical inactivity or low cardiorespiratory fitness levels are strongly associated with the development of metabolic syndrome. Although this association has been studied extensively in adults, little is known regarding this association in adolescents. The aim of this study was to analyze the association between physical activity and cardiorespiratory fitness levels with metabolic syndrome in Brazilian adolescents.</p> <p>Methods</p> <p>A random sample of 223 girls (mean age, 14.4 ± 1.6 years) and 233 boys (mean age, 14.6 ± 1.6 years) was selected for the study. The level of physical activity was determined by the Bouchard three-day physical activity record. Cardiorespiratory fitness was estimated by the Leger 20-meter shuttle run test. The metabolic syndrome components assessed included waist circumference, blood pressure, HDL-cholesterol, triglycerides, and fasting plasma glucose levels. Independent Student <it>t</it>-tests were used to assess gender differences. The associations between physical activity and cardiorespiratory fitness with the presence of metabolic syndrome were calculated using logistic regression models adjusted for age and gender.</p> <p>Results</p> <p>A high prevalence of metabolic syndrome was observed in inactive adolescents (males, 11.4%; females, 7.2%) and adolescents with low cardiorespiratory fitness levels (males, 13.9%; females, 8.6%). A significant relationship existed between metabolic syndrome and low cardiorespiratory fitness (OR, 3.0 [1.13-7.94]).</p> <p>Conclusion</p> <p>The prevalence of metabolic syndrome is high among adolescents who are inactive and those with low cardiorespiratory fitness. Prevention strategies for metabolic syndrome should concentrate on enhancing fitness levels early in life.</p

Text Mining in Cancer Gene and Pathway Prioritization

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.National Library of Medicine (U.S.) (Grant U54LM008748)Scullen Family Group for Cancer Data Analysi