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Network-based inference

Rafael Ceschin, Alexandria Zahner, William Reynolds, Jenna Gaesser, Giulio Zuccoli, Cecilia W Lo, Vanathi Gopalakrishnan, Ashok Panigrahy
Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance...
May 21, 2018: NeuroImage
Ali Zarezade, Sina Jafarzadeh, Hamid R Rabiee
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited...
2018: PloS One
Anurag Passi, Neeraj Kumar Rajput, David J Wild, Anshu Bhardwaj
Tuberculosis (TB) is the world's leading infectious killer with 1.8 million deaths in 2015 as reported by WHO. It is therefore imperative that alternate routes of identification of novel anti-TB compounds are explored given the time and costs involved in new drug discovery process. Towards this, we have developed RepTB. This is a unique drug repurposing approach for TB that uses molecular function correlations among known drug-target pairs to predict novel drug-target interactions. In this study, we have created a Gene Ontology based network containing 26,404 edges, 6630 drug and 4083 target nodes...
May 21, 2018: Journal of Cheminformatics
Philipp Berens, Jeremy Freeman, Thomas Deneux, Nicolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H Macke, Srinivas C Turaga, Patrick Mineault, Peter Rupprecht, Stephan Gerhard, Rainer W Friedrich, Johannes Friedrich, Liam Paninski, Marius Pachitariu, Kenneth D Harris, Ben Bolte, Timothy A Machado, Dario Ringach, Jasmine Stone, Luke E Rogerson, Nicolas J Sofroniew, Jacob Reimer, Emmanouil Froudarakis, Thomas Euler, Miroslav Román Rosón, Lucas Theis, Andreas S Tolias, Matthias Bethge
In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing...
May 21, 2018: PLoS Computational Biology
Timothy J Tse, Lorne E Doig, Song Tang, Xiaohui Zhang, Weimin Sun, Steve B Wiseman, Cindy Xin Feng, Hongling Liu, John P Giesy, Markus Hecker, Paul D Jones
Freshwaters worldwide are under increasing pressure from anthropogenic activities and changing climate. Unfortunately, many inland waters lack sufficient long-term monitoring to assess environmental trends. Analysis of sedimentary ancient DNA (sedaDNA) is emerging as a means to reconstruct the past occurrence of microbial communities of inland waters. The purpose of this study was to assess a combination of high-throughput sequencing (16S rRNA) of sedaDNA and traditional palaeolimnological analyses to explore multidecadal relationships among cyanobacterial community composition, the potential for cyanotoxin production and palaeoenvironmental proxies...
May 21, 2018: Environmental Science & Technology
Adrien Rougny, Pauline Gloaguen, Nathalie Langonné, Eric Reiter, Pascale Crépieux, Anne Poupon, Christine Froidevaux
With the dramatic increase of the diversity and the sheer quantity of biological data generated, the construction of comprehensive signaling networks that include precise mechanisms cannot be carried out manually anymore. In this context, we propose a logic-based method that allows building large signaling networks automatically. Our method is based on a set of expert rules that make explicit the reasoning made by biologists when interpreting experimental results coming from a wide variety of experiment types...
May 18, 2018: Scientific Reports
Zachary P Kilpatrick
Information from preceding trials of cognitive tasks can bias performance in the current trial, a phenomenon referred to as interference. Subjects performing visual working memory tasks exhibit interference in their responses: the recalled target location is biased in the direction of the target presented on the previous trial. We present modeling work that develops a probabilistic inference model of this history-dependent bias, and links our probabilistic model to computations of a recurrent network wherein short-term facilitation accounts for the observed bias...
May 18, 2018: Scientific Reports
David Hallac, Youngsuk Park, Stephen Boyd, Jure Leskovec
Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL) , a method of inferring time-varying networks from raw time series data...
August 2017: KDD: Proceedings
Kranthi Varala, Amy Marshall-Colón, Jacopo Cirrone, Matthew D Brooks, Angelo V Pasquino, Sophie Léran, Shipra Mittal, Tara M Rock, Molly B Edwards, Grace J Kim, Sandrine Ruffel, W Richard McCombie, Dennis Shasha, Gloria M Coruzzi
This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our "just-in-time" analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to "prune" the network to 155 TFs and 608 targets...
May 16, 2018: Proceedings of the National Academy of Sciences of the United States of America
Kevin Faust, Quin Xie, Dominick Han, Kartikay Goyle, Zoya Volynskaya, Ugljesa Djuric, Phedias Diamandis
BACKGROUND: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce...
May 16, 2018: BMC Bioinformatics
Jiyang Yu, Jose M Silva
The Connectivity Map (CMAP) project profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments. As most targeted anticancer therapeutics aim to induce tumor-selective apoptosis, it is critical to understand the specific cell death pathways triggered by drugs. This can help to better understand the mechanism of how cancer cells respond to chemical stimulations and improve the treatment of human tumors. In this study, using Connectivity MAP microarray-based gene expression data, we applied a Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular pathway...
2018: Methods in Molecular Biology
D Okada, S Endo, H Matsuda, S Ogawa, Y Taniguchi, T Katsuta, T Watanabe, H Iwaisaki
Genome-wide association studies (GWAS) of quantitative traits have detected numerous genetic associations, but they encounter difficulties in pinpointing prominent candidate genes and inferring gene networks. The present study used a systems genetics approach integrating GWAS results with external RNA-expression data to detect candidate gene networks in feed utilization and growth traits of Japanese Black cattle, which are matters of concern. A SNP co-association network was derived from significant correlations between SNPs with effects estimated by GWAS across seven phenotypic traits...
May 12, 2018: Journal of Animal Science
Duc-Hau Le, Lan T M Dao
Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs...
May 11, 2018: Journal of Molecular Biology
Giles L Colclough, Mark W Woolrich, Samuel J Harrison, Pedro A Rojas López, Pedro A Valdes-Sosa, Stephen M Smith
A Bayesian model for sparse, hierarchical inverse covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fmri, meg and eeg data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions...
May 7, 2018: NeuroImage
Mathias Foo, Iulia Gherman, Peijun Zhang, Declan G Bates, Katherine Denby
Crop disease leads to significant waste world-wide, both pre- and post-harvest, with subsequent economic and sustainability consequences. Disease outcome is determined both by the plants' response to the pathogen and by the ability of the pathogen to suppress defense responses and manipulate the plant to enhance colonization. The defense response of a plant is characterized by significant transcriptional reprogramming mediated by underlying gene regulatory networks and components of these networks are often targeted by attacking pathogens...
May 10, 2018: ACS Synthetic Biology
Kai Chen, Hai Yan Yu, Ji Wei Zhang, Bei Xin Wang, Qiu Wen Chen
Improving the stability of integrity of biotic index (IBI; i.e., multi-metric indices, MMI) across temporal and spatial scales is one of the most important issues in water ecosystem integrity bioassessment and water environment management. Using datasets of field-based macroinvertebrate and physicochemical variables and GIS-based natural predictors (e.g., geomorphology and climate) and land use variables collected at 227 river sites from 2004 to 2011 across the Zhejiang Province, China, we used random forests (RF) to adjust the effects of natural variations at temporal and spatial scales on macroinvertebrate metrics...
June 18, 2017: Ying Yong Sheng Tai Xue Bao, the Journal of Applied Ecology
Isabelle Ripp, Anna-Nora Zur Nieden, Sonja Blankenagel, Nicolai Franzmeier, Johan N Lundström, Jessica Freiherr
In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory...
May 7, 2018: Human Brain Mapping
Robert Langner, Susanne Leiberg, Felix Hoffstaedter, Simon B Eickhoff
Self-regulation refers to controlling our emotions and actions in the pursuit of higher-order goals. Although research suggests commonalities in the cognitive control of emotion and action, evidence for a shared neural substrate is scant and largely circumstantial. Here we report on two large-scale meta-analyses of human neuroimaging studies on emotion or action control, yielding two fronto-parieto-insular networks. The networks' overlap, however, was restricted to four brain regions: posteromedial prefrontal cortex, bilateral anterior insula, and right temporo-parietal junction...
May 3, 2018: Neuroscience and Biobehavioral Reviews
Haibo Wang, Hanjie Chen, Ming Cai
The primary objective of this study was to develop an evaluation method for assessing an urban traffic noise-exposed population and apply it in the main urban area of Guangzhou. The method based on points of interest (POIs) and noise map is realized in several steps. First, after regionalizing based on road networks and executing a cluster analysis for regions according to the properties of POIs, the environmental noise functional regions (NFRs) of the urban area are presented. Then, surrounding POIs are used to infer the type of buildings, and according to the attraction of different building types and the whole population of the region, the population distribution at the building level is calculated...
May 2, 2018: Environmental Pollution
Zhen Chao, Dohyeon Kim, Hee-Joung Kim
In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. Recently, neural network technique was applied to medical image fusion by many researchers, but there are still many deficiencies. In this study, we propose a novel fusion method to combine multi-modality medical images based on the enhanced fuzzy radial basis function neural network (Fuzzy-RBFNN), which includes five layers: input, fuzzy partition, front combination, inference, and output...
April 2018: Physica Medica: PM
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