Statistical inference for a quasi birth-death model of RNA transcription.

Background: A birth-death process of which the births follow a hypoexponential distribution with L phases and are controlled by an on/off mechanism, is a population process which we call the on/off-seq-L process. It is a suitable model for the dynamics of a population of RNA molecules in a single living cell. Motivated by this biological application, our aim is to develop a statistical method to estimate the model parameters of the on/off-seq-L process, based on observations of the population size at discrete time points, and to apply this method to real RNA data.

Analysis of Twitter data with the Bayesian fused graphical lasso.

We propose a method to simplify textual Twitter data into understandable networks of terms that can signify important events and their possible changes over time. The method allows for common characteristics of the networks across time periods and each period can comprise multiple unknown sub-networks. The networks are described by Gaussian graphical models and their parameter values are estimated through a Bayesian approach with a fused lasso-type prior on the precision matrices of the underlying mixtures of the sub-models.

Bayesian mixture regression analysis for regulation of Pluripotency in ES cells.

Background: Observed levels of gene expression strongly depend on both activity of DNA binding transcription factors (TFs) and chromatin state through different histone modifications (HMs). In order to recover the functional relationship between local chromatin state, TF binding and observed levels of gene expression, regression methods have proven to be useful tools. They have been successfully applied to predict mRNA levels from genome-wide experimental data and they provide insight into context-dependent gene regulatory mechanisms.

Reconstruction of molecular network evolution from cross-sectional omics data.

Cross-sectional studies may shed light on the evolution of a disease like cancer through the comparison of patient traits among disease stages. This problem is especially challenging when a gene-gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage-wise mixtures of Gaussian graphical models (GGMs) from high-dimensional data.

Gene Network Reconstruction using Global-Local Shrinkage Priors.

Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done in the neighbourhood of each node or gene.

A three domain covariance framework for EEG/MEG data.

In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed.

A morpho-density approach to estimating neural connectivity.

Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity.

Independently outgrowing neurons and geometry-based synapse formation produce networks with realistic synaptic connectivity.

Neuronal signal integration and information processing in cortical networks critically depend on the organization of synaptic connectivity. During development, neurons can form synaptic connections when their axonal and dendritic arborizations come within close proximity of each other. Although many signaling cues are thought to be involved in guiding neuronal extensions, the extent to which accidental appositions between axons and dendrites can already account for synaptic connectivity remains unclear.

Comparison of targeted maximum likelihood and shrinkage estimators of parameters in gene networks.

Gene regulatory networks, in which edges between nodes describe interactions between transcription factors (TFs) and their target genes, model regulatory interactions that determine the cell-type and condition-specific expression of genes. Regression methods can be used to identify TF-target gene interactions from gene expression and DNA sequence data. The response variable, i.

Identification of context-specific gene regulatory networks with GEMULA--gene expression modeling using LAsso.

Motivation: Gene regulatory networks, in which edges between nodes describe interactions between transcriptional regulators and their target genes, determine the coordinated spatiotemporal expression of genes. Especially in higher organisms, context-specific combinatorial regulation by transcription factors (TFs) is believed to determine cellular states and fates. TF-target gene interactions can be studied using high-throughput techniques such as ChIP-chip or ChIP-Seq.

Novel candidate genes associated with hippocampal oscillations.

The hippocampus is critical for a wide range of emotional and cognitive behaviors. Here, we performed the first genome-wide search for genes influencing hippocampal oscillations. We measured local field potentials (LFPs) using 64-channel multi-electrode arrays in acute hippocampal slices of 29 BXD recombinant inbred mouse strains.

LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data.

All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g.

Growth references for height, weight and body mass index of twins aged 0-2.5 years.

Aim: To determine the size of the growth deficit in Dutch monozygotic and dizygotic twins aged 0-2.5 years as compared to singletons and to construct reference growth charts for twins.

Methods: Growth of twins was studied using longitudinal data on over 4000 twins aged 0-2.

Identification of candidate transcriptional modulators involved in successful regeneration after nerve injury.

Successful regeneration of injured neurons requires a complex molecular response that involves the expression, modification and transport of a large number of proteins. The identity of neuronal proteins responsible for the initiation of regenerative neurite outgrowth is largely unknown. Dorsal root ganglion (DRG) neurons display robust and successful regeneration following lesion of their peripheral neurite, whereas outgrowth of central neurites is weak and does not lead to functional recovery.

On the power for linkage detection using a test based on scan statistics.

We analyze some aspects of scan statistics, which have been proposed to help for the detection of weak signals in genetic linkage analysis. We derive approximate expressions for the power of a test based on moving averages of the identity by descent allele sharing proportions for pairs of relatives at several contiguous markers. We confirm these approximate formulae by simulation.

Genetic study of the height and weight process during infancy.

Longitudinal height and weight data from 4649 Dutch twin pairs between birth and 2.5 years of age were analyzed. The data were first summarized into parameters of a polynomial of degree 4 by a mixed-effects procedure.

Morphine exposure and abstinence define specific stages of gene expression in the rat nucleus accumbens.

Intermittent exposure to addictive drugs causes long-lasting changes in responsiveness to these substances due to persistent molecular and cellular alterations within the meso-corticolimbic system. In this report, we studied the expression profiles of 159 genes in the rat nucleus accumbens during morphine exposure (14 days, 10 mg/kg s.c.

Search for haplotype interactions that influence susceptibility to type 1 diabetes, through use of unphased genotype data.

Type 1 diabetes is a T-cell-mediated chronic disease characterized by the autoimmune destruction of pancreatic insulin-producing beta cells and complete insulin deficiency. It is the result of a complex interrelation of genetic and environmental factors, most of which have yet to be identified. Simultaneous identification of these genetic factors, through use of unphased genotype data, has received increasing attention in the past few years.

Exploring heterogeneity in tumour data using Markov chain Monte Carlo.

We describe a Bayesian approach to incorporate between-individual heterogeneity associated with parameters of complicated biological models. We emphasize the use of the Markov chain Monte Carlo (MCMC) method in this context and demonstrate the implementation and use of MCMC by analysis of simulated overdispersed Poisson counts and by analysis of an experimental data set on preneoplastic liver lesions (their number and sizes) in the presence of heterogeneity. These examples show that MCMC-based estimates, derived from the posterior distribution with uniform priors, may agree well with maximum likelihood estimates (if available).