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Network-based inference

Mauro Ursino, Cristiano Cuppini, Elisa Magosso
Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference...
January 17, 2017: Neural Computation
Yunzhi Wang, Olivier David, Xiaoping Hu, Gopikrishna Deshpande
PURPOSE: Investigating directional interactions between brain regions plays a critical role in fully understanding brain function. Consequently, multiple methods have been developed for noninvasively inferring directional connectivity in human brain networks using functional MRI (fMRI). Recent simulations by Smith et al showed that Patel's τ, a method based on higher-order statistics, was the best approach for inferring directional interactions from fMRI. Because simulations make restrictive assumptions about reality, we set out to verify this finding using experimental fMRI data obtained from a three-region network in a rat model with electrophysiological validation...
January 16, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Rina Gendelman, Heming Xing, Olga K Mirzoeva, Preeti Sarde, Christina Curtis, Heidi Feiler, Paul McDonagh, Joe W Gray, Iya Khalil, W Michael Korn
Molecular networks governing cellular responses to targeted therapies are complex dynamic systems with non-intuitive behaviors. Here we applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell cycle arrest caused by inhibition of MEK1/2. Through simulation of reverse-engineered Bayesian network modeling, we generated predictions of G1-S transition...
January 13, 2017: Cancer Research
Viktoria Kindzierski, Silvia Raschke, Nicole Knabe, Frank Siedler, Beatrix Scheffer, Katharina Pflüger-Grau, Friedhelm Pfeiffer, Dieter Oesterhelt, Alberto Marin-Sanguino, Hans-Jörg Kunte
Halophilic bacteria use a variety of osmoregulatory methods, such as the accumulation of one or more compatible solutes. The wide diversity of compounds that can act as compatible solute complicates the task of understanding the different strategies that halophilic bacteria use to cope with salt. This is specially challenging when attempting to go beyond the pathway that produces a certain compatible solute towards an understanding of how the metabolic network as a whole addresses the problem. Metabolic reconstruction based on genomic data together with Flux Balance Analysis (FBA) is a promising tool to gain insight into this problem...
2017: PloS One
Ming Hao, Stephen H Bryant, Yanli Wang
In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors...
January 12, 2017: Scientific Reports
Yu-Dong Cai, Qing Zhang, Yu-Hang Zhang, Lei Chen, Tao Huang
Tumor metastasis is defined as the spread of tumor cells from one organ or part to another that is not directly connected to it, which significantly contributes to the progression and aggravation of tumorigenesis. Because it always involves multiple organs, the metastatic process is difficult to study in its entirety. Complete identification of the genes related to this process is an alternative way to study metastasis. In this study, we developed a computational method to identify such genes. To test our method, we selected breast cancer bone metastasis...
January 11, 2017: Journal of Proteome Research
Fabrizio De Vico Fallani, Vito Latora, Mario Chavez
In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections...
January 11, 2017: PLoS Computational Biology
Inez M Greven, Richard Ramsey
The majority of human neuroscience research has focussed on understanding functional organisation within segregated patches of cortex. The ventral visual stream has been associated with the detection of physical features such as faces and body parts, whereas the theory-of-mind network has been associated with making inferences about mental states and underlying character, such as whether someone is friendly, selfish, or generous. To date, however, it is largely unknown how such distinct processing components integrate neural signals...
January 6, 2017: Neuropsychologia
W Sun, M Jiang, F Yin
PURPOSE: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). METHODS: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study...
June 2016: Medical Physics
Joseph J Nalluri, Debmalya Barh, Vasco Azevedo, Preetam Ghosh
Decoding the patterns of miRNA regulation in diseases are important to properly realize its potential in diagnostic, prog- nostic, and therapeutic applications. Only a handful of studies computationally predict possible miRNA-miRNA interactions; hence, such interactions require a thorough investigation to understand their role in disease progression. In this paper, we design a novel computational pipeline to predict the common signature/core sets of miRNA-miRNA interactions for different diseases using network inference algorithms on the miRNA-disease expression profiles; the individual predictions of these algorithms were then merged using a consensus-based approach to predict miRNA-miRNA associations...
January 3, 2017: Scientific Reports
Mohammad Javad Anbari, Massoud Tabesh, Abbas Roozbahani
In wastewater systems as one of the most important urban infrastructures, the adverse consequences and effects of unsuitable performance and failure event can sometimes lead to disrupt part of a city functioning. By identifying high failure risk areas, inspections can be implemented based on the system status and thus can significantly increase the sewer network performance. In this study, a new risk assessment model is developed to prioritize sewer pipes inspection using Bayesian Networks (BNs) as a probabilistic approach for computing probability of failure and weighted average method to calculate the consequences of failure values...
December 29, 2016: Journal of Environmental Management
Diana M Hendrickx, Terezinha Souza, Danyel G J Jennen, Jos C S Kleinjans
Unravelling gene regulatory networks (GRNs) influenced by chemicals is a major challenge in systems toxicology. Because toxicant-induced GRNs evolve over time and dose, the analysis of global gene expression data measured at multiple time points and doses will provide insight in the adverse effects of compounds. Therefore, there is a need for mathematical methods for GRN identification from time-over-dose-dependent data. One of the current approaches for GRN inference is Time Series Network Identification (TSNI)...
December 28, 2016: Archives of Toxicology
Shun Guo, Qingshan Jiang, Lifei Chen, Donghui Guo
BACKGROUND: Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS: We introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with p genes into p subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm...
December 28, 2016: BMC Bioinformatics
Tianfu Wu, Yang Lu, Song-Chun Zhu
This paper presents a method, called AOGTracker, for simultaneously tracking, learning and parsing (TLP) of unknown objects in video sequences with a hierarchical and compositional And-Or graph (AOG) representation. The TLP method is formulated in the Bayesian framework with a spatial and a temporal dynamic programming (DP) algorithms inferring object bounding boxes on-the-fly. During online learning, the AOG is discriminatively learned using latent SVM [1] to account for appearance (e.g., lighting and partial occlusion) and structural (e...
December 23, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Luis F Jover, Justin Romberg, Joshua S Weitz
In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infect which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable...
November 2016: Royal Society Open Science
Xiao-Tai Huang, Yuan Zhu, Leanne Lai Hang Chan, Zhongying Zhao, Hong Yan
MOTIVATION: Cell fate specification plays a key role to generate distinct cell types during metazoan development. However, most of the underlying signaling networks at cellular level are not well understood. Availability of time lapse single-cell gene expression data collected throughout C. elegans embryogenesis provides an excellent opportunity for investigating signaling networks underlying cell fate specification at systems, cellular and molecular levels. RESULTS: We propose a framework to infer signaling networks at cellular level by exploring the single-cell gene expression data...
December 23, 2016: Bioinformatics
Yifeng Li, Fang-Xiang Wu, Alioune Ngom
Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association...
December 22, 2016: Briefings in Bioinformatics
Maxime Pelland, Pierre Orban, Christian Dansereau, Franco Lepore, Pierre Bellec, Olivier Collignon
Resting-state functional connectivity (RSFC) studies have provided strong evidences that visual deprivation influences the brain's functional architecture. In particular, reduced RSFC coupling between occipital (visual) and temporal (auditory) regions has been reliably observed in early blind individuals (EB) at rest. In contrast, task-dependent activation studies have repeatedly demonstrated enhanced co-activation and connectivity of occipital and temporal regions during auditory processing in EB. To investigate this apparent discrepancy, the functional coupling between temporal and occipital networks at rest was directly compared to that of an auditory task in both EB and sighted controls (SC)...
December 21, 2016: NeuroImage
Mashor Housh, Ziv Ohar
The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network...
December 8, 2016: Water Research
Mehdi Sadeghi, Bijan Ranjbar, Mohamad Reza Ganjalikhany, Faiz M Khan, Ulf Schmitz, Olaf Wolkenhauer, Shailendra K Gupta
Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks...
2016: PloS One
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