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Network-based inference

Yuelong Guo, Sylwia Fudali, Jacinta Gimeno, Peter DiGennaro, Stella Chang, Valerie M Williamson, David McK Bird, Dahlia M Nielsen
Organisms engage in extensive cross-species molecular dialogue, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an expression quantitative trait locus (eQTL) mapping-based approach to identify cause-and-effect relationships between genes from two partners engaged in an interspecific interaction. We demonstrated the approach by assaying expression of ninety-eight isogenic plants (Medicago truncatula), each inoculated with a genetically distinct line of the diploid parasitic nematode Meloidogyne hapla With this design, systematic differences in gene expression across host plants could be mapped to genetic polymorphisms of their infecting parasites...
June 22, 2017: Genetics
Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps...
June 15, 2017: IEEE Transactions on Medical Imaging
Rasmus Magnusson, Guido Pio Mariotti, Mattias Köpsén, William Lövfors, Danuta R Gawel, Rebecka Jörnsten, Jörg Linde, Torbjörn Nordling, Elin Nyman, Sylvie Schulze, Colm E Nestor, Huan Zhang, Gunnar Cedersund, Mikael Benson, Andreas Tjärnberg, Mika Gustafsson
Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets...
June 22, 2017: PLoS Computational Biology
Jens Christian Claussen, Jurgita Skiecevičienė, Jun Wang, Philipp Rausch, Tom H Karlsen, Wolfgang Lieb, John F Baines, Andre Franke, Marc-Thorsten Hütt
The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns...
June 2017: PLoS Computational Biology
Katrin Töpner, Guilherme J M Rosa, Daniel Gianola, Chris-Carolin Schön
Relationships among traits were investigated on the genomic and residual levels using novel methodology. This included inference on these relationships via Bayesian networks and an assessment of the networks with structural equation models. The methodology employed three steps. First, a Bayesian multiple-trait Gaussian model was fitted to the data to decompose phenotypic values into their genomic and residual components. Second, genomic and residual network structures among traits were learned from estimates of these two components...
June 21, 2017: G3: Genes—Genomes—Genetics
Ricardo de Souza Jacomini, David Correa Martins-Jr, Felipe Leno da Silva, Anna Helena Reali Costa
Gene network (GN) inference from temporal gene expression data is a crucial and challenging problem in systems biology. Expression data sets usually consist of dozens of temporal samples, while networks consist of thousands of genes, thus rendering many inference methods unfeasible in practice. To improve the scalability of GN inference methods, we propose a novel framework called GeNICE, based on probabilistic GNs; the main novelty is the introduction of a clustering procedure to group genes with related expression profiles and to provide an approximate solution with reduced computational complexity...
June 21, 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
Francesca Vitali, Simone Marini, Martina Balli, Hanne Grosemans, Maurilio Sampaolesi, Yves A Lussier, Maria Gabriella Cusella De Angelis, Riccardo Bellazzi
The molecular mechanisms underlying tissue regeneration and wound healing are still poorly understood despite their importance. In this paper we develop a bioinformatics approach, combining biology and network theory to drive experiments for better understanding the genetic underpinnings of wound healing mechanisms and for selecting potential drug targets. We start by selecting literature-relevant genes in murine wound healing, and inferring from them a Protein-Protein Interaction (PPI) network. Then, we analyze the network to rank wound healing-related genes according to their topological properties...
June 21, 2017: Pharmaceuticals
Dimitrios D Alexakis, Filippos-Dimitrios K Mexis, Anthi-Eirini K Vozinaki, Ioannis N Daliakopoulos, Ioannis K Tsanis
A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested...
June 21, 2017: Sensors
Wenbin Guo, Cristiane P G Calixto, Nikoleta Tzioutziou, Ping Lin, Robbie Waugh, John W S Brown, Runxuan Zhang
BACKGROUND: Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required...
June 19, 2017: BMC Systems Biology
Hui Shen, Huaze Xu, Lubing Wang, Yu Lei, Liu Yang, Peng Zhang, Jian Qin, Ling-Li Zeng, Zongtan Zhou, Zheng Yang, Dewen Hu
Past studies on drawing group inferences for functional magnetic resonance imaging (fMRI) data usually assume that a brain region is involved in only one functional brain network. However, recent evidence has demonstrated that some brain regions might simultaneously participate in multiple functional networks. Here, we presented a novel approach for making group inferences using sparse representation of resting-state fMRI data and its application to the identification of changes in functional networks in the brains of 37 healthy young adult participants after 36 h of sleep deprivation (SD) in contrast to the rested wakefulness (RW) stage...
June 19, 2017: Human Brain Mapping
M T D Albuquerque, S Gerassis, C Sierra, J Taboada, J E Martín, I M H R Antunes, J R Gallego
Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain)...
June 15, 2017: Science of the Total Environment
Maria Angels de Luis Balaguer, Rosangela Sozzani
Gene regulatory network (GRN) models have been shown to predict and represent interactions among sets of genes. Here, we first show the basic steps to implement a simple but computationally efficient algorithm to infer GRNs based on dynamic Bayesian networks (DBNs), and we then explain how to approximate DBN-based GRN models with continuous models. In addition, we show a MATLAB implementation of the key steps of this method, which we use to infer an Arabidopsis root GRN.
2017: Methods in Molecular Biology
Nooshin Omranian, Zoran Nikoloski
The goal of the gene regulatory network (GRN) inference is to determine the interactions between genes given heterogeneous data capturing spatiotemporal gene expression. Since transcription underlines all cellular processes, the inference of GRN is the first step in deciphering the determinants of the dynamics of biological systems. Here, we first describe the generic steps of the inference approaches that rely on similarity measures and group the similarity measures based on the computational methodology used...
2017: Methods in Molecular Biology
Tak Lee, Insuk Lee
Functional gene networks link genes based on their functional relatedness, which is inferred from various complementary biological datasets. Gene networks comprising vast amounts of data can be used to predict which genes are associated with complex traits. Decades of studies in plant biology using the model organism Arabidopsis thaliana have generated large amounts of information, enabling the development of a system-level molecular network. AraNet (currently version 2) is a genome-scale functional gene network for Arabidopsis thaliana, constructed by integrating 19 types of genomics data and can be explored through a web-server (http://www...
2017: Methods in Molecular Biology
Borja Esteve-Altava, Toni Vallès-Català, Roger Guimerà, Marta Sales-Pardo, Diego Rasskin-Gutman
Craniosynostosis, the premature fusion of cranial bones, affects the correct development of the skull producing morphological malformations in newborns. To assess the susceptibility of each craniofacial articulation to close prematurely, we used a network model of the skull to quantify the link reliability (an index based on stochastic block models and Bayesian inference) of each articulation. We show that, of the 93 human skull articulations at birth, the few articulations that are associated with non-syndromic craniosynostosis conditions have statistically significant lower reliability scores than the others...
June 13, 2017: Scientific Reports
Xun Li, Xiang Li
Temporal networks have opened a new dimension in defining and quantification of complex interacting systems. Our ability to identify and reproduce time-resolved interaction patterns is, however, limited by the restricted access to empirical individual-level data. Here we propose an inverse modelling method based on first-arrival observations of the diffusion process taking place on temporal networks. We describe an efficient coordinate-ascent implementation for inferring stochastic temporal networks that builds in particular but not exclusively on the null model assumption of mutually independent interaction sequences at the dyadic level...
June 12, 2017: Nature Communications
Niu Qiao, Song-Mei Wang, Jin-Xia Wang, Bin Kang, Shan-Shan Zhen, Xin-Jiang Zhang, Zhi-Yong Hao, Jing-Chen Ma, Chao Qiu, Yu-Liang Zhao, Lei Liu, Xuan-Yi Wang
To understand the distribution of genotyping, as well as evolution of norovirus circulating among children<5yrs., a population-based diarrhea surveillance targeted children<5yrs. was conducted in rural Zhengding County, Hebei Province, China between October 2011 and March 2012. RT-PCR was used to amplify the capsid-encoding region of GI and GII norovirus to identify norovirus infection. All PCR products were sequenced and analyzed for genotyping and constructing phylogenetic tree. Dynamic distribution network was constructed by TempNet to illustrate the genetic relationships at two different time points...
June 7, 2017: Infection, Genetics and Evolution
Gianfranco Politano, Federica Logrand, Mara Brancaccio, Stefano Di Carlo
In most developed countries, cardiovascular diseases are among the top causes of death and their development has been shown closely related to aging. In this context, because of their ability to pervasively influence gene networks, miRs have been found as possible key players in the development of cardiac pathologies, suggesting their potential role as therapeutic targets or diagnostic markers. Based on these assumptions, we hereby present a computational study that applies data fusion techniques coupled with network analysis theory to identify a regulatory model able to represent the relationship between key genes and miRs involved in cardiac senescence processes...
June 7, 2017: Methods: a Companion to Methods in Enzymology
Mina Jafari, Behnam Ghavami, Vahid Sattari
The inference of Gene Regulatory Networks (GRNs) using gene expression data in order to detect the basic cellular processes is a key issue in biological systems. Inferring GRN correctly requires inferring predictor set accurately. In this paper, a fast and accurate predictor set inference framework which linearly combines some inference methods is proposed. The purpose of the combination of various methods is to increase the accuracy of inferred GRN. The proposed framework offers a linear weighted combination of Pearson Correlation Coefficient (PCC) and two different feature selection approaches, namely: Information Gain (IG) and ReliefF...
June 8, 2017: Artificial Intelligence in Medicine
Sehi L'Yi, Daekyoung Jung, Minsik Oh, Bohyoung Kim, Robert J Freishtat, Mamta Giri, Eric Hoffman, Jinwook Seo
In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs. However, there still is a need for improving the performance for miRNA prediction algorithms and more importantly for interactive visualization tools for an integrative analysis of multiple prediction results...
June 6, 2017: Methods: a Companion to Methods in Enzymology
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