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https://www.readbyqxmd.com/read/28528256/evaluation-of-artificial-time-series-microarray-data-for-dynamic-gene-regulatory-network-inference
#1
P Xenitidis, I Seimenis, S Kakolyris, A Adamopoulos
High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process...
May 17, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28526529/adding-biological-meaning-to-human-protein-protein-interactions-identified-by-yeast-two-hybrid-screenings-a-guide-through-bioinformatics-tools
#2
Juliana Felgueiras, Joana Vieira Silva, Margarida Fardilha
"A man is known by the company he keeps" is a popular expression that perfectly fits proteins. A common approach to characterize the function of a target protein is to identify its interacting partners and thus infer its roles based on the known functions of the interactors. Protein-protein interaction networks (PPINs) have been created for several organisms, including humans, primarily as results of high-throughput screenings, such as yeast two-hybrid (Y2H). Their unequivocal use to understand events underlying human pathophysiology is promising in identifying genes and proteins associated with diseases...
May 16, 2017: Journal of Proteomics
https://www.readbyqxmd.com/read/28522969/equilibrium-propagation-bridging-the-gap-between-energy-based-models-and-backpropagation
#3
Benjamin Scellier, Yoshua Bengio
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28510607/recent-evolutionary-history-of-chrysoperla-externa-hagen-1861-neuroptera-chrysopidae-in-brazil
#4
Adriana C Morales-Corrêa E Castro, Nara Cristina Chiarini Pena Barbosa
This work aimed to elucidate the distribution of Chrysoperla externa haplotypes and investigate whether it exhibits structure based on genetic composition as opposed to geographic location. The genetic diversity of C. externa, analyzed by AMOVA using the COI and 16S rRNA genes as mitochondrial markers, showed significant haplotype structure arising from genetic differences that was not associated with sampling location. This was reflected in the network grouping. Bayesian inference showed that haplotype distribution may have its origins in C...
2017: PloS One
https://www.readbyqxmd.com/read/28505781/statistical-inference-for-community-detection-in-signed-networks
#5
Xuehua Zhao, Bo Yang, Xueyan Liu, Huiling Chen
The problem of community detection in networks has received wide attention and proves to be computationally challenging. In recent years, with the surge of signed networks with positive links and negative links, to find community structure in such signed networks has become a research focus in the area of network science. Although many methods have been proposed to address the problem, their performance seriously depends on the predefined optimization objectives or heuristics which are usually difficult to accurately describe the intrinsic structure of community...
April 2017: Physical Review. E
https://www.readbyqxmd.com/read/28505156/an-independent-component-analysis-confounding-factor-correction-framework-for-identifying-broad-impact-expression-quantitative-trait-loci
#6
Jin Hyun Ju, Sushila A Shenoy, Ronald G Crystal, Jason G Mezey
Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation...
May 15, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28500050/a-systemic-analysis-of-transcriptomic-and-epigenomic-data-to-reveal-regulation-patterns-for-complex-disease
#7
Chao Xu, Ji-Gang Zhang, Dongdong Lin, Lan Zhang, Hui Shen, Hong-Wen Deng
Integrating diverse genomics data can provide a global view of the complex biological processes related to the human complex diseases. Although substantial efforts have been made to integrate different omics data, there are at least three challenges for multi-omics integration methods: (i) How to simultaneously consider the effects of various genomic factors, since these factors jointly influence the phenotypes; (ii) How to effectively incorporate the information from publicly accessible databases and omics datasets to fully capture the interactions among (epi-)genomic factors from diverse omics data; and (iii) Until present, the combination of >2 omics datasets has been poorly explored...
May 12, 2017: G3: Genes—Genomes—Genetics
https://www.readbyqxmd.com/read/28495332/the-neural-correlates-of-theory-of-mind-and-their-role-during-empathy-and-the-game-of-chess-a-functional-magnetic-resonance-imaging-study
#8
Joanne L Powell, Davide Grossi, Rhiannon Corcoran, Fernand Gobet, Marta García-Fiñana
Chess involves the capacity to reason iteratively about potential intentional choices of an opponent and therefore involves high levels of explicit theory of mind [ToM] (i.e. ability to infer mental states of others) alongside clear, strategic rule-based decision-making. Functional magnetic resonance imaging was used on 12 healthy male novice chess players to identify cortical regions associated with chess, ToM and empathizing. The blood-oxygen-level-dependent (BOLD) response for chess and empathizing tasks was extracted from each ToM region...
May 8, 2017: Neuroscience
https://www.readbyqxmd.com/read/28489543/integrating-multiple-heterogeneous-networks-for-novel-lncrna-disease-association-inference
#9
Jingpu Zhang, Zuping Zhang, Zhigang Chen, Lei Deng
Accumulating experimental evidence has indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes implicated in many human diseases. However, only relatively few experimentally supported lncRNA-disease associations have been reported. Developing effective computational methods to infer lncRNA-disease associations is becoming increasingly important. Current network-based algorithms typically use a network representation to identify novel associations between lncRNAs and diseases...
May 4, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28484519/study-of-meta-analysis-strategies-for-network-inference-using-information-theoretic-approaches
#10
Ngoc C Pham, Benjamin Haibe-Kains, Pau Bellot, Gianluca Bontempi, Patrick E Meyer
BACKGROUND: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets...
2017: BioData Mining
https://www.readbyqxmd.com/read/28478794/inferring-causal-structures-and-comparing-the-causal-effects-among-calving-difficulty-gestation-length-and-calf-size-in-japanese-black-cattle
#11
K Inoue, M Hosono, Y Tanimoto
The objectives of this study were to infer phenotypic causal networks involving gestation length (GL) and calving difficulty (CD) for the primiparity of 1850 Japanese Black heifers, and the birth weight (BWT), withers height (WH) and chest girth (CHG) of their full blood calves, and to compare the causal effects among them. The inductive causation (IC) algorithm was employed to search for causal links among these traits; it was applied to the posterior distribution of the residual (co)variance matrix of a multiple-trait sire-maternal grand sire (MGS) model...
May 8, 2017: Animal: An International Journal of Animal Bioscience
https://www.readbyqxmd.com/read/28477207/from-protein-protein-interactions-to-protein-co-expression-networks-a-new-perspective-to-evaluate-large-scale-proteomic-data
#12
REVIEW
Danila Vella, Italo Zoppis, Giancarlo Mauri, Pierluigi Mauri, Dario Di Silvestre
The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations...
December 2017: EURASIP Journal on Bioinformatics & Systems Biology
https://www.readbyqxmd.com/read/28472402/cofactor-improved-protein-function-prediction-by-combining-structure-sequence-and-protein-protein-interaction-information
#13
Chengxin Zhang, Peter L Freddolino, Yang Zhang
The COFACTOR web server is a unified platform for structure-based multiple-level protein function predictions. By structurally threading low-resolution structural models through the BioLiP library, the COFACTOR server infers three categories of protein functions including gene ontology, enzyme commission and ligand-binding sites from various analogous and homologous function templates. Here, we report recent improvements of the COFACTOR server in the development of new pipelines to infer functional insights from sequence profile alignments and protein-protein interaction networks...
May 2, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28472169/identifying-novel-fruit-related-genes-in-arabidopsis-thaliana-based-on-the-random-walk-with-restart-algorithm
#14
Yunhua Zhang, Li Dai, Ying Liu, YuHang Zhang, ShaoPeng Wang
Fruit is essential for plant reproduction and is responsible for protection and dispersal of seeds. The development and maturation of fruit is tightly regulated by numerous genetic factors that respond to environmental and internal stimulation. In this study, we attempted to identify novel fruit-related genes in a model organism, Arabidopsis thaliana, using a computational method. Based on validated fruit-related genes, the random walk with restart (RWR) algorithm was applied on a protein-protein interaction (PPI) network using these genes as seeds...
2017: PloS One
https://www.readbyqxmd.com/read/28471111/intelligent-diagnosis-of-jaundice-with-dynamic-uncertain-causality-graph-model
#15
Shao-Rui Hao, Shi-Chao Geng, Lin-Xiao Fan, Jia-Jia Chen, Qin Zhang, Lan-Juan Li
Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors...
May 2017: Journal of Zhejiang University. Science. B
https://www.readbyqxmd.com/read/28470036/choosing-face-the-curse-of-self-in-profile-image-selection
#16
David White, Clare A M Sutherland, Amy L Burton
People draw automatic social inferences from photos of unfamiliar faces and these first impressions are associated with important real-world outcomes. Here we examine the effect of selecting online profile images on first impressions. We model the process of profile image selection by asking participants to indicate the likelihood that images of their own face ("self-selection") and of an unfamiliar face ("other-selection") would be used as profile images on key social networking sites. Across two large Internet-based studies (n = 610), in line with predictions, image selections accentuated favorable social impressions and these impressions were aligned to the social context of the networking sites...
2017: Cognitive Research: Principles and Implications
https://www.readbyqxmd.com/read/28469391/a-mixture-copula-bayesian-network-model-for-multimodal-genomic-data
#17
Qingyang Zhang, Xuan Shi
Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28468991/inferring-neuronal-network-functional-connectivity-with-directed-information
#18
Zhiting Cai, Curtis L Neveu, Douglas A Baxter, John H Byrne, Behnaam Aazhang
A major challenge in neuroscience is to develop effective tools that infer the circuit connectivity from large-scale recordings of neuronal activity patterns. In this paper, context tree maximizing (CTM) was used to estimate directed information (DI), which measures causal influences among neural spike trains in order to infer putative synaptic connections. In contrast to existing methods, the method presented here is data-driven and can readily identify both linear and nonlinear relations between neurons. This CTM-DI method reliably identified circuit structures underlying simulations of realistic conductance-based networks...
May 3, 2017: Journal of Neurophysiology
https://www.readbyqxmd.com/read/28466811/arnetmit-r-package-association-rules-based-gene-co-expression-networks-of-mirna-targets
#19
M Özgür Cingiz, G Biricik, B Diri
miRNAs are key regulators that bind to target genes to suppress their gene expression level. The relations between miRNA-target genes enable users to derive co-expressed genes that may be involved in similar biological processes and functions in cells. We hypothesize that target genes of miRNAs are co-expressed, when they are regulated by multiple miRNAs. With the usage of these co-expressed genes, we can theoretically construct co-expression networks (GCNs) related to 152 diseases. In this study, we introduce ARNetMiT that utilize a hash based association rule algorithm in a novel way to infer the GCNs on miRNA-target genes data...
March 31, 2017: Cellular and Molecular Biology
https://www.readbyqxmd.com/read/28462535/neural-systems-for-evaluating-speaker-un-believability
#20
Xiaoming Jiang, Ryan Sanford, Marc D Pell
Our voice provides salient cues about how confident we sound, which promotes inferences about how believable we are. However, the neural mechanisms involved in these social inferences are largely unknown. Employing functional magnetic resonance imaging, we examined the brain networks and individual differences underlying the evaluation of speaker believability from vocal expressions. Participants (n = 26) listened to statements produced in a confident, unconfident, or "prosodically unmarked" (neutral) voice, and judged how believable the speaker was on a 4-point scale...
April 30, 2017: Human Brain Mapping
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