Joint Network Inference for Time-Course Data
Semiparametric approaches to inference in joint models for longitudinal and time-to-event data
... profiles for ten randomly-chosen subjects from ACTG 175 and suggests that the un- derlying trend is well-approximated by a straight line after week twelve; as only nine events occurred prior to this time, we ...
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Joint inference of misaligned irregular time series with application to Greenland ice core data
... raw data for any single core are irregular in ...in time. After processing, such data are made available to researchers as regu- lar time series: a data ...the joint processing ...
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Successful network inference from time-series data using mutual information rate
... We first derive analytically an expression for the Mutual Information Rate MIR, namely, the amount of information exchanged per unit of time, that can be used to estimate the MIR between[r] ...
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Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data
... coupled data sources. In our case these data sources are the pol-II occupancy over time collected at different locations along the transcribed region of a ...the data as a convolved process ...
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Prediction of the Network Administration Course Results Based on Fuzzy Inference
... this course in the spring semester next ...numerical data-based prediction ...interpolation-based inference technique and a rule base optimization method based on the clonal selection principle that ...
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Real-time inference in a VLSI spiking neural network
... we plot the neuron activities in the feed-forward and sWTA only conditions, for comparison. The raw data showing the spiking activity on population Z for a different trial is shown in Fig. 3. The most common ...
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Nonparametric inference for unbalance time series data
... t=T X 1(Y t ≤ z) are the empirical dis- tribution functions. Here, z are grid points whose cardinality L(T ) increases with sample size. In this case, the limiting null distribution is ∆ F = sup z W F (z), where W F is a ...
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Approximate inference of gene regulatory network models from RNA-Seq time series data
... synthetic data, for 5 subnetworks sampled from the ...regulatory network was not contained in the 95% credible interval of the corresponding regression coefficients, and for the Lasso and glmnet methods, ...
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Structural network inference from time-series data using a generative model and transfer entropy
... sents the true positive count of the i-th class. The higher these index value, the better the performance of distinguishing the different degree of disease severity. For the different methods studied, the average ...
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Systematic analysis of time resolved high-throughput data using stochastic network inference methods
... on network structures directly, in order to increase or decrease the weight for specific ...prior network that covers the important interactions? Clearly, the choice of the source of prior knowledge heavily ...
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SiGNet: A signaling network data simulator to enable signaling network inference.
... these data and achieved a Pearson correlation with the real data of up to ...The network depicted in Fig 3A shows the interactions between EGFR and a number of downstream proteins, as described in ...
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Consensus Network Inference of Microarray Gene Expression Data
... including data from the DREAM4 ...expression data is that the target network is known in ...and time series gene expression datasets (Table ...expression data contain both. The use ...
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Joint Label Inference in Networks
... social network (Chakrabarti et ...Google+ network (Gong et ...movie network, where EdgeExplain outperforms label propagation as well as other competing methods (Zheleva and Getoor, 2009; Yin et ...
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A Comparison of the Functional Modules Identified from Time Course and Static PPI Network Data
... dynamic network analysis is essential for further understanding of molecular ...affects network interactions, topology and function ...died, time course analysis becomes an important ...
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Brian Caffo-Statistical inference for data science - A companion to the Coursera Statistical Inference Course (2015).pdf
... The Poisson distribution is used to model counts. It is perhaps only second to the normal distribution usefulness. In fact, the Bernoulli, binomial and multinomial distributions can all be modeled by clever uses of the ...
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CSI : A nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data
... from time series mRNA expression data are only able to cope with single time series (or single perturbations with biological replicates), it is becoming increasingly common for several time ...
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Predictive inference generation in the cerebral hemispheres: An investigation of time course and reader goals
... the Time Course Hypothesis (Koivisto, 1997), it was predicted that at an earlier time point in predictive inference processing (a 500 ms SOA), both strongly and weakly constrained inferential ...
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Enabling network inference methods to handle missing data and outliers
... synthetic network for benchmarking reverse-engineering ...true network is known, and at the same time the system outputs can be measured in vivo, instead of just simulated in ...of time series ...
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Dynamic interaction network inference from longitudinal microbiome data
... current time slice is determined by parameters from both intra and inter edges, thus, modeling the complex interactions and dynamics between the entities in the microbial ...the network structure and ...
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