Home About us Contact
|
Home About us Contact | ||
|
|
||
Interaction Models (interaction + models)
Selected AbstractsTowards Proteome,Wide Interaction Models Using the Proteochemometrics ApproachMOLECULAR INFORMATICS, Issue 6-7 2010Helena Strömbergsson Abstract A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pKi) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80,% and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes. [source] GENETIC STUDY: FULL ARTICLE: Incorporating age at onset of smoking into genetic models for nicotine dependence: evidence for interaction with multiple genesADDICTION BIOLOGY, Issue 3 2010Richard A. Grucza ABSTRACT Nicotine dependence is moderately heritable, but identified genetic associations explain only modest portions of this heritability. We analyzed 3369 SNPs from 349 candidate genes and investigated whether incorporation of SNP-by-environment interaction into association analyses might bolster gene discovery efforts and prediction of nicotine dependence. Specifically, we incorporated the interaction between allele count and age at onset of regular smoking (AOS) into association analyses of nicotine dependence. Subjects were from the Collaborative Genetic Study of Nicotine Dependence and included 797 cases ascertained for Fagerström nicotine dependence and 811 non-nicotine-dependent smokers as controls, all of European descent. Compared with main effect models, SNP × AOS interaction models resulted in higher numbers of nominally significant tests, increased predictive utility at individual SNPs and higher predictive utility in a multi-locus model. Some SNPs previously documented in main effect analyses exhibited improved fits in the joint analysis, including rs16969968 from CHRNA5 and rs2314379 from MAP3K4. CHRNA5 exhibited larger effects in later-onset smokers, in contrast with a previous report that suggested the opposite interaction (Weiss et al. 2008). However, a number of SNPs that did not emerge in main effect analyses were among the strongest findings in the interaction analyses. These include SNPs located in GRIN2B (P = 1.5 × 10,5), which encodes a subunit of the N-methyl-D-aspartate receptor channel, a key molecule in mediating age-dependent synaptic plasticity. Incorporation of logically chosen interaction parameters, such as AOS, into genetic models of substance use disorders may increase the degree of explained phenotypic variation and constitutes a promising avenue for gene discovery. [source] An Empirically Based Implementation and Evaluation of a Hierarchical Model for Commuting FlowsGEOGRAPHICAL ANALYSIS, Issue 3 2010Jens Petter Gitlesen This article provides an empirical evaluation of a hierarchical approach to modeling commuting flows. As the gravity family of spatial interaction models represents a benchmark for empirical evaluation, we begin by reviewing basic aspects of these models. The hierarchical modeling framework is the same that Thorsen, Ubøe, and Nævdal (1999) used. However, because some modifications are required to construct a more workable model, we undertake a relatively detailed presentation of the model, rather than merely referring to the presentation in Thorsen, Ubøe, and Nævdal (1999). The model uses a hierarchical specification of a transportation network and the individual search procedure. Journeys to work are determined by the effects of distance deterrence and of intervening opportunities, and by the location of potential destinations relative to alternatives at subsequent levels in a transportation network. The model calibration uses commuting data from a region in western Norway. The estimated parameter values are reasonable, and the explanatory power is very satisfactory when compared with the results of a competing destinations approach. Este artículo presenta una evaluación empírica de un enfoque jerárquico para el modelado de flujos de desplazamientos del lugar de residencia al lugar de trabajo (commuting flows). Los modelos interacción espacial, y en particular los modelos de gravedad representan un buen punto de referencia para esta tarea. Por esta razón, los autores inician el estudio con una revisión de los aspectos básicos de estos modelos. El marco general del modelo jerárquico seleccionado es el mismo que emplean Thorsen, Ubøe y Nævdal (1999). Sin embargo, debido a que algunas modificaciones son necesarias para construir un método más viable, los autores presentan su versión del modelo de manera detallada en lugar de sólo hacer referencia a la versión de Thorsen, Ubøe y Nævdal. El modelo modificado propuesto emplea una especificación jerárquica para una red de transporte y hace uso de un procedimiento de búsqueda individual (individual search procedure). Los desplazamientos hacia el lugar de trabajo son establecidos en base a 1) los efectos limitantes de distancia de las oportunidades de desplazamiento, y 2) la localización de los posibles destinos medida en relación a las distintas alternativas existentes en los niveles inferiores de la jerarquía de la red de transporte. La calibración del modelo utiliza datos de desplazamientos de una región en el oeste de Noruega. Finalmente, los autores concluyen que los valores de los parámetros estimados obtenidos son razonables, y que el poder explicativo del modelo es muy satisfactorio en comparación a los resultados obtenidos por un análisis comparativo/competitivo de destinos a (competing destinations). [source] Predictors of middle childhood psychosomatic problems: An emotion regulation approachINFANT AND CHILD DEVELOPMENT, Issue 5 2004Berit Hagekull Abstract Development of the psychosomatic problems picky eating and headache and stomachache complaints in middle childhood was investigated from an emotion regulation perspective. The role of negative emotionality and family emotion regulatory factors (attachment to mother and parental perceived control) was studied. The sample (N=87) was a predominantly middleclass, community sample. The study was longitudinal, based on data from several data collections between child age 11 months and 9 years. The results showed that headache and stomachache complaints were mainly predicted by early negative emotionality, and picky eating by the family factors. More negative emotionality, insecure attachment and less perceived control were related to more psychosomatic problems in linear and interaction models. The findings were interpreted as showing that by considering emotion regulation, a fruitful perspective for understanding the development of psychosomatic problems could be elaborated. Copyright © 2004 John Wiley & Sons, Ltd. [source] Pharmacodynamic interaction of recombinant human interleukin-10 and prednisolone using in vitro whole blood lymphocyte proliferationJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 5 2002Abhijit Chakraborty Abstract Prednisolone, a commonly used synthetic corticosteroid, and IL-10, a cytokine under investigation for strong antiinflammatory properties, are being contemplated as a potential joint therapeutic regimen in immune disorders. Their pharmacodynamic interactions were examined in blood from healthy adult male and female volunteers using an in vitro phytohemagglutinin (PHA)-stimulated whole,blood lymphocyte proliferation technique. Isobolograms along with parametric competitive and noncompetitive interaction models were used to determine the nature and intensity of interactions. Single drug effects show prednisolone more efficacious in inhibiting lymphocyte proliferation with an IC50 of 3.3 ng/mL and Imax value of 1, signifying complete suppression. Analogous parameters for IL-10 were 16.2 ng/mL for IC50 and 0.89 for Imax. There were no significant differences in the single drug immunosuppressive effects among genders. Their joint effects showed additive interaction based on isobolographic analysis. Parametric analysis using the competitive interaction model described their interaction as slightly synergistic, while the noncompetitive interaction modeling indicate a small degree of antagonism. Also, the joint effects in females tend to be more antagonistic than males. Concomitant use of prednisolone and IL-10 should thus reflect the net additive responses to concentrations of each agent. © 2002 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 91:1334,1342, 2002 [source] Towards Proteome,Wide Interaction Models Using the Proteochemometrics ApproachMOLECULAR INFORMATICS, Issue 6-7 2010Helena Strömbergsson Abstract A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pKi) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80,% and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes. [source] Coupling 3D and 1D fluid-structure interaction models for blood flow simulationsPROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2006L. Formaggia Three-dimensional (3D) simulations of blood flow in medium to large vessels are now a common practice. These models consist of the 3D Navier-Stokes equations for incompressible Newtonian fluids coupled with a model for the vessel wall structure. However, it is still computationally unaffordable to simulate very large sections, let alone the whole, of the human circulatory system with fully 3D fluid-structure interaction models. Thus truncated 3D regions have to be considered. Reduced models, one-dimensional (1D) or zero-dimensional (0D), can be used to approximate the remaining parts of the cardiovascular system at a low computational cost. These models have a lower level of accuracy, since they describe the evolution of averaged quantities, nevertheless they provide useful information which can be fed to the more complex model. More precisely, the 1D models describe the wave propagation nature of blood flow and coupled with the 3D models can act also as absorbing boundary conditions. We consider in this work the coupling of a 3D fluid-structure interaction model with a 1D hyperbolic model. We study the stability of the coupling and present some numerical results. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Specification and estimation of social interaction models with network structuresTHE ECONOMETRICS JOURNAL, Issue 2 2010Lung-fei Lee Summary, This paper considers the specification and estimation of social interaction models with network structures and the presence of endogenous, contextual and correlated effects. With macro group settings, group-specific fixed effects are also incorporated in the model. The network structure provides information on the identification of the various interaction effects. We propose a quasi-maximum likelihood approach for the estimation of the model. We derive the asymptotic distribution of the proposed estimator, and provide Monte Carlo evidence on its small sample performance. [source] Mixtures of correlated bosons and fermions: Dynamical mean-field theory for normal and condensed phasesANNALEN DER PHYSIK, Issue 9 2009K. Byczuk Abstract We derive a dynamical mean-field theory for mixtures of interacting bosons and fermions on a lattice (BF-DMFT). The BF-DMFT is a comprehensive, thermodynamically consistent framework for the theoretical investigation of Bose-Fermi mixtures and is applicable for arbitrary values of the coupling parameters and temperatures. It becomes exact in the limit of high spatial dimensions d or coordination number Z of the lattice. In particular, the BF-DMFT treats normal and condensed bosons on equal footing and thus includes the effects caused by their dynamic coupling. Using the BF-DMFT we investigate two different interaction models of correlated lattice bosons and fermions, one where all particles are spinless (model I) and one where fermions carry a spin one-half (model II). In model I the local, repulsive interaction between bosons and fermions can give rise to an attractive effective interaction between the bosons. In model II it can also lead to an attraction between the fermions. [source] Evaluating the Ability of Tree-Based Methods and Logistic Regression for the Detection of SNP-SNP InteractionANNALS OF HUMAN GENETICS, Issue 3 2009M. García-Magariños Summary Most common human diseases are likely to have complex etiologies. Methods of analysis that allow for the phenomenon of epistasis are of growing interest in the genetic dissection of complex diseases. By allowing for epistatic interactions between potential disease loci, we may succeed in identifying genetic variants that might otherwise have remained undetected. Here we aimed to analyze the ability of logistic regression (LR) and two tree-based supervised learning methods, classification and regression trees (CART) and random forest (RF), to detect epistasis. Multifactor-dimensionality reduction (MDR) was also used for comparison. Our approach involves first the simulation of datasets of autosomal biallelic unphased and unlinked single nucleotide polymorphisms (SNPs), each containing a two-loci interaction (causal SNPs) and 98 ,noise' SNPs. We modelled interactions under different scenarios of sample size, missing data, minor allele frequencies (MAF) and several penetrance models: three involving both (indistinguishable) marginal effects and interaction, and two simulating pure interaction effects. In total, we have simulated 99 different scenarios. Although CART, RF, and LR yield similar results in terms of detection of true association, CART and RF perform better than LR with respect to classification error. MAF, penetrance model, and sample size are greater determining factors than percentage of missing data in the ability of the different techniques to detect true association. In pure interaction models, only RF detects association. In conclusion, tree-based methods and LR are important statistical tools for the detection of unknown interactions among true risk-associated SNPs with marginal effects and in the presence of a significant number of noise SNPs. In pure interaction models, RF performs reasonably well in the presence of large sample sizes and low percentages of missing data. However, when the study design is suboptimal (unfavourable to detect interaction in terms of e.g. sample size and MAF) there is a high chance of detecting false, spurious associations. [source] Ion channel formation and membrane-linked pathologies of misfolded hydrophobic proteins: The role of dangerous unchaperoned moleculesCLINICAL AND EXPERIMENTAL PHARMACOLOGY AND PHYSIOLOGY, Issue 9 2002Joseph I Kourie Summary 1.,Protein,membrane interaction includes the interaction of proteins with intrinsic receptors and ion transport pathways and with membrane lipids. Several hypothetical interaction models have been reported for peptide-induced membrane destabilization, including hydrophobic clustering, electrostatic interaction, electrostatic followed by hydrophobic interaction, wedge × type incorporation and hydrophobic mismatch. 2.,The present review focuses on the hypothesis of protein interaction with lipid membranes of those unchaperoned positively charged and misfolded proteins that have hydrophobic regions. We advance the hypothesis that protein misfolding that leads to the exposure of hydrophobic regions of proteins renders them potentially cytotoxic. Such proteins include prion, amyloid , protein (A,P), amylin, calcitonin, serum amyloid and C-type natriuretic peptides. These proteins have the ability to interact with lipid membranes, thereby inducing membrane damage and cell malfunction. 3.,We propose that the most significant mechanism of membrane damage induced by hydrophobic misfolded proteins is mediated via the formation of ion channels. The hydrophobicity based toxicity of several proteins linked to neurodegenerative pathologies is similar to those observed for antibacterial toxins and viral proteins. 4.,It is hypothesized that the membrane damage induced by amyloids, antibacterial toxins and viral proteins represents a common mechanism for cell malfunction, which underlies the associated pathologies and cytotoxicity of such proteins. [source] | |||||||||||||