Gene Expression Prediction by Soft Integration and the Elastic Net—Best Performance of the DREAM3 Gene Expression Challenge

Background: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance. Methodology/Principal Findings: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance. Conclusions/Significance: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.CENII

Logistik - ohne Zeitwirtschaft nicht denkbar : Zeitfaktor bestimmt wirtschaftlichen Ressourceneinsatz

Standen bisher bei den umfassend in der Literatur behandelten Logistikansätzen mehr die Materialwirtschaftsprobleme hinsichtlich der zeitgerechten Beschaffung und der Bestandshöhe im Vordergrund der Betrachtung, so meldet sich jetzt die Zeitwirtschaft mit den damit verbundenen Anstößen auf die Unternehmensstrategie zurück. Der Zeitfaktor ist bei kapitalintensiver Produktion schon immer ein knappes Gut gewesen, mit dem gut gewirtschaftet werden musste, um wettbewerbsfähig zu bleiben. Die derzeitige Wettbewerbssituation mit immer kürzeren Produkt-Entwicklungszeiten, Produkt-Lebenszeiten, Produkt-Herstellzeiten, Produkt-Lieferzeiten verstärkt die enorme Bedeutung des Zeitfaktors als Wert oder Bezugsgröße für Kosten, Termine oder Kapazitäten

Combined Multivariate and Pathway Analyses Show That Allergen-Induced Gene Expression Changes in CD4+ T Cells Are Reversed by Glucocorticoids

Background: Glucocorticoids (GCs) play a key role in the treatment of allergy. However, the genome-wide effects of GCs on gene expression in allergen-challenged CD4(+) T cells have not been described. The aim of this study was to perform a genome-wide analysis to investigate whether allergen-induced gene expression changes in CD4(+) T cells could be reversed by GCs. Methodology/Principal Findings: Gene expression microarray analysis was performed to profile gene expression in diluent( D), allergen- (A), and allergen + hydrocortisone- (T) challenged CD4(+) T cells from patients with seasonal allergic rhinitis. Principal component analysis (PCA) showed good separation of the three groups. To identify the correlation between changes in gene expression in allergen-challenged CD4(+) T cells before and after GC treatment, we performed orthogonal partial least squares discriminant analysis (OPLS-DA) followed by Pearson correlation analysis. This revealed that allergen-induced genes were widely reversed by GC treatment (r = -0.77, Pless than0.0001). We extracted 547 genes reversed by GC treatment from OPLS-DA models based on their high contribution to the discrimination and found that those genes belonged to several different inflammatory pathways including TNFR2 Signalling, Interferon Signalling, Glucocorticoid Receptor Signalling and T Helper Cell Differentiation. The results were supported by gene expression microarray analyses of two independent materials. Conclusions/Significance: Allergen-induced gene expression changes in CD4(+) T cells were reversed by treatment with glucocorticoids. The top allergen-induced genes that reversed by GC treatment belonged to several inflammatory pathways and genes of known or potential relevance for allergy

Comparison and validation of community structures in complex networks

The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.Comment: To appear in Physica A; 25 page

Hybrid Modelling for Stroke Care: Review and suggestions of new approaches for risk assessment and simulation of scenarios

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke

Intensity-based dual model method for generation of synthetic CT images from standard T2-weighted MR images - Generalized technique for four different MR scanners

Background and purpose: Recent studies have shown that it is possible to conduct entire radiotherapy treatment planning (RTP) workflow using only MR images. This study aims to develop a generalized intensity-based method to generate synthetic CT (sCT) images from standard T2-weighted (T2(W)) MR images of the pelvis. Materials and methods: This study developed a generalized dual model HU conversion method to convert standard T2(W) MR image intensity values to synthetic HU values, separately inside and outside of atlas-segmented bone volume contour. The method was developed and evaluated with 20 and 35 prostate cancer patients, respectively. MR images with scanning sequences in clinical use were acquired with four different MR scanners of three vendors. Results: For the generated synthetic CT (sCT) images of the 35 prostate patients, the mean (and maximal) HU differences in soft and bony tissue volumes were 16 +/- 6 HUs (34 HUs) and -46 +/- 56 HUs (181 HUs), respectively, against the true CT images. The average of the PTV mean dose difference in sCTs compared to those in true CTs was -0.6 +/- 0.4% (-1.3%). Conclusions: The study provides a generalized method for sCT creation from standard T2(W) images of the pelvis. The method produced clinically acceptable dose calculation results for all the included scanners and MR sequences. (c) 2017 Elsevier B.V. All rights reserved.Peer reviewe

Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment

Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles

Digital twins to personalize medicine

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient