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network inference

William Chad Young, Adrian E Raftery, Ka Yee Yeung
Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach...
December 1, 2016: Mathematical Biosciences and Engineering: MBE
Hui Liu, Fan Zhang, Shital Kumar Mishra, Shuigeng Zhou, Jie Zheng
Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data...
October 24, 2016: Scientific Reports
Maryam Songhorzadeh, Karim Ansari-Asl, Alimorad Mahmoudi
Quantifying delayed directional couplings between electroencephalographic (EEG) time series requires an efficient method of causal network inference. This is especially due to the limited knowledge about the underlying dynamics of the brain activity. Recent methods based on information theoretic measures such as Transfer Entropy (TE) made significant progress on this issue by providing a model-free framework for causality detection. However, TE estimation from observed data is not a trivial task, especially when the number of variables is large which is the case in a highly complex system like human brain...
October 14, 2016: Computers in Biology and Medicine
Khursheed Ahmad, Ved P Kumar, Bheem Dutt Joshi, Mohamed Raza, Parag Nigam, Anzara Anjum Khan, Surendra P Goyal
BACKGROUND: The Tibetan antelope (Pantholops hodgsonii), or chiru, is an endangered antelope, distributed in China [Xinjiang, Xizang, Qinghai, Zhuolaihu Lake (Breeding habitat)], and India (Aksai Chin and Ladakh). There is a global demand for the species prized wool, which is used in weaving shahtoosh shawls. Over the years, the population of the Tibetan antelope has drastically declined from more than a million to a few thousand individuals, mainly due to poaching. Field studies undertaken in Ladakh, India also indicated winter migration of the population to Tibet...
October 21, 2016: BMC Research Notes
Alexander Lück, Verena Wolf
BACKGROUND: Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks. RESULTS: We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data...
October 21, 2016: BMC Systems Biology
Erich Kummerfeld, Joseph Ramsey
Many scientific research programs aim to learn the causal structure of real world phenomena. This learning problem is made more difficult when the target of study cannot be directly observed. One strategy commonly used by social scientists is to create measurable "indicator" variables that covary with the latent variables of interest. Before leveraging the indicator variables to learn about the latent variables, however, one needs a measurement model of the causal relations between the indicators and their corresponding latents...
2016: KDD: Proceedings
Daniel V Guebel, Néstor V Torres
Motivation: In the brain of elderly-healthy individuals, the effects of sexual dimorphism and those due to normal aging appear overlapped. Discrimination of these two dimensions would powerfully contribute to a better understanding of the etiology of some neurodegenerative diseases, such as "sporadic" Alzheimer. Methods: Following a system biology approach, top-down and bottom-up strategies were combined. First, public transcriptome data corresponding to the transition from adulthood to the aging stage in normal, human hippocampus were analyzed through an optimized microarray post-processing (Q-GDEMAR method) together with a proper experimental design (full factorial analysis)...
2016: Frontiers in Aging Neuroscience
Kevin A McGoff, Xin Guo, Anastasia Deckard, Christina M Kelliher, Adam R Leman, Lauren J Francey, John B Hogenesch, Steven B Haase, John L Harer
We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle...
October 19, 2016: Genome Biology
Daifeng Wang, Fei He, Sergei Maslov, Mark Gerstein
Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem's gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally-e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution...
October 2016: PLoS Computational Biology
Yoonsik Shim, Andrew Philippides, Kevin Staras, Phil Husbands
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events...
October 2016: PLoS Computational Biology
A Kibleur, G Gras-Combe, D Benis, J Bastin, T Bougerol, S Chabardès, M Polosan, O David
High-frequency deep brain stimulation of the subthalamic nucleus can be used to treat severe obsessive-compulsive disorders that are refractory to conventional treatments. The mechanisms of action of this approach possibly rely on the modulation of associative-limbic subcortical-cortical loops, but remain to be fully elucidated. Here in 12 patients, we report the effects of high-frequency stimulation of the subthalamic nucleus on behavior, and on electroencephalographic responses and inferred effective connectivity during motor inhibition processes involved in the stop signal task...
October 18, 2016: Translational Psychiatry
Nídia Cangi, Jonathan L Gordon, Laure Bournez, Valérie Pinarello, Rosalie Aprelon, Karine Huber, Thierry Lefrançois, Luís Neves, Damien F Meyer, Nathalie Vachiéry
The disease, Heartwater, caused by the Anaplasmataceae E. ruminantium, represents a major problem for tropical livestock and wild ruminants. Up to now, no effective vaccine has been available due to a limited cross protection of vaccinal strains on field strains and a high genetic diversity of Ehrlichia ruminantium within geographical locations. To address this issue, we inferred the genetic diversity and population structure of 194 E. ruminantium isolates circulating worldwide using Multilocus Sequence Typing based on lipA, lipB, secY, sodB, and sucA genes...
2016: Frontiers in Cellular and Infection Microbiology
Jie Zhou, Pianyu Zhong, Tinghui Zhang
Determination of sequence similarity is one of the major steps in computational phylogenetic studies. One of the major tasks of computational biologists is to develop novel mathematical descriptors for similarity analysis. DNA clustering is an important technology that automatically identifies inherent relationships among large-scale DNA sequences. The comparison between the DNA sequences of different species helps determine phylogenetic relationships among species. Alignment-free approaches have continuously gained interest in various sequence analysis applications such as phylogenetic inference and metagenomic classification/clustering, particularly for large-scale sequence datasets...
2016: Evolutionary Bioinformatics Online
Lianshuo Li, Zicheng Wang, Peng He, Shining Ma, Jie Du, Rui Jiang
Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network is in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network...
October 13, 2016: Genomics, Proteomics & Bioinformatics
Wenqing Fu, Sharon R Browning, Brian L Browning, Joshua M Akey
Identifying and characterizing genomic regions that are shared identical by descent (IBD) among individuals can yield insight into population history, facilitate the identification of adaptively evolving loci, and be an important tool in disease gene mapping. Although increasingly large collections of exome sequences have been generated, it is challenging to detect IBD segments in exomes, precluding many potentially informative downstream analyses. Here, we describe an approach, ExIBD, to robustly detect IBD segments in exome-sequencing data, rigorously evaluate its performance, and apply this method to high-coverage exomes from 6,515 European and African Americans...
October 6, 2016: American Journal of Human Genetics
Bethany Lusch, Pedro D Maia, J Nathan Kutz
Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure...
September 2016: Physical Review. E
Ulrich Nübel
Driven by progress of DNA sequencing technologies, recent population genomics studies have revealed that several bacterial pathogens constitute 'measurably evolving populations'. As a consequence, it was possible to reconstruct the emergence and spatial spread of drug-resistant bacteria on the basis of temporally structured samples of bacterial genome sequences. Based on currently available data, some general inferences can be drawn across different bacterial species as follows: (1) Resistance to various antibiotics evolved years to decades earlier than had been anticipated on the basis of epidemiological surveillance data alone...
October 15, 2016: Current Topics in Microbiology and Immunology
Anatoly Yambartsev, Michael A Perlin, Yevgeniy Kovchegov, Natalia Shulzhenko, Karina L Mine, Xiaoxi Dong, Andrey Morgun
BACKGROUND: Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. RESULTS: We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches...
October 13, 2016: Biology Direct
Hussein A Hejase, Kevin J Liu
BACKGROUND: Branching events in phylogenetic trees reflect bifurcating and/or multifurcating speciation and splitting events. In the presence of gene flow, a phylogeny cannot be described by a tree but is instead a directed acyclic graph known as a phylogenetic network. Both phylogenetic trees and networks are typically reconstructed using computational analysis of multi-locus sequence data. The advent of high-throughput sequencing technologies has brought about two main scalability challenges: (1) dataset size in terms of the number of taxa and (2) the evolutionary divergence of the taxa in a study...
October 13, 2016: BMC Bioinformatics
Sabine Gollner, Heiko Stuckas, Terue C Kihara, Stefan Laurent, Sahar Kodami, Pedro Martinez Arbizu
Communities in spatially fragmented deep-sea hydrothermal vents rich in polymetallic sulfides could soon face major disturbance events due to deep-sea mineral mining, such that unraveling patterns of gene flow between hydrothermal vent populations will be an important step in the development of conservation policies. Indeed, the time required by deep-sea populations to recover following habitat perturbations depends both on the direction of gene flow and the number of migrants available for re-colonization after disturbance...
2016: PloS One
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