Ridge estimation of network models from time-course omics data

2018 ◽  
Vol 61 (2) ◽  
pp. 391-405 ◽  
Author(s):  
Viktorian Miok ◽  
Saskia M. Wilting ◽  
Wessel N. van Wieringen
Keyword(s):  
Time Course ◽  
Network Models ◽  
Omics Data ◽  
Ridge Estimation

Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data

2016 ◽  
Vol 59 (1) ◽  
pp. 172-191 ◽  
Author(s):  
Viktorian Miok ◽  
Saskia M. Wilting ◽  
Wessel N. van Wieringen
Keyword(s):  
Time Series ◽  
Time Course ◽  
Omics Data ◽  
Chain Graph ◽  
Ridge Estimation

scholarly journals Asymptotic behavior of the node degrees in the ensemble average of adjacency matrix

2016 ◽  
Vol 4 (3) ◽  
pp. 385-399 ◽  
Author(s):  
YUKIO HAYASHI
Keyword(s):  
Asymptotic Behavior ◽  
Adjacency Matrix ◽  
General Framework ◽  
Time Course ◽  
Network Models ◽  
Explicit Representation ◽  
Topological Network ◽  
Growing Networks ◽  
Theoretical Analyses ◽  
Increasing Function

AbstractVarious important and useful quantities or measures that characterize the topological network structure are usually investigated for a network, then they are averaged over the samples. In this paper, we propose an explicit representation by the beforehand averaged adjacency matrix over samples of growing networks as a new general framework for investigating the characteristic quantities. It is applied to some network models, and shows a good approximation of degree distribution asymptotically. In particular, our approach will be applicable through the numerical calculations instead of intractable theoretical analyses, when the time-course of degree is a monotone increasing function like power law or logarithm.

A neural network model of the effects of entrenchment and memory development on grammatical gender learning

2012 ◽  
Vol 16 (2) ◽  
pp. 246-265 ◽  
Author(s):  
DEREK MONNER ◽  
KAREN VATZ ◽  
GIOVANNA MORINI ◽  
SO-ONE HWANG ◽  
ROBERT DeKEYSER
Keyword(s):  
Neural Network ◽  
Time Course ◽  
Age Of Onset ◽  
Network Models ◽  
Memory Development ◽  
Neural Network Models ◽  
Gender Assignment ◽  
Less Is More ◽  
Onset Delays ◽  
The Individual

To investigate potential causes of L2 performance deficits that correlate with age of onset, we use a computational model to explore the individual contributions of L1 entrenchment and aspects of memory development. Since development and L1 entrenchment almost invariably coincide, studying them independently is seldom possible in humans. To avoid this confound, we study neural network models that learn to solve gender assignment and agreement tasks in Spanish and French. We model the learner as a collection of recurrent cell assemblies that subserve working memory and are facilitated by trainable long-term connections. Varying the time-course over which assemblies and connections are added allows us to compare small, growing, child-like networks to fixed-size adult-like ones. Networks undergo variable-length exposure to L1 before L2 onset to control the amount of L1 entrenchment. This model, by allowing us independent control of both variables, lends us a novel glimpse of all sides of their interaction and affords a rare test of the less-is-more hypothesis. Network comparisons suggest that final L2 proficiency declines as L2 onset delays increase relative to L1, implicating an L1 entrenchment effect. However, aspects of memory development during learning play a key role in mitigating these impairments, lending support to less-is-more as a contributor to sensitive periods.

scholarly journals DynOmics to identify delays and co-expression patterns across time course experiments

2016 ◽  
Author(s):  
Jasmin Straube ◽  
Bevan Emma Huang ◽  
Kim-Anh Lê Cao
Keyword(s):  
Time Course ◽  
Expression Patterns ◽  
Time Integration ◽  
Omics Data ◽  
Multiple Sources ◽  
Combining Data ◽  
The Difference ◽  
Changes Over Time ◽  
High Degree ◽  
Molecular Expression

ABSTRACTDynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in ‘omics’ data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across ‘omics’ data within an organism and for comparative gene expression analysis across organisms.

Integrating –omics data into genome-scale metabolic network models: principles and challenges

2018 ◽  
Vol 62 (4) ◽  
pp. 563-574 ◽  
Author(s):  
Charlotte Ramon ◽  
Mattia G. Gollub ◽  
Jörg Stelling
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Network Models ◽  
Omics Data ◽  
Scale Models ◽  
Common Framework ◽  
Genome Scale ◽  
Constraint Based Models ◽  
Omics Data Integration

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available –omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of –omics data into CBMs focussing on the methods’ assumptions and limitations. We argue that key assumptions – often derived from single-enzyme kinetics – do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for –omics data integration in a common framework to provide more accurate predictions.

scholarly journals Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

2017 ◽  
Author(s):  
Genevieve L. Stein-O’Brien ◽  
Raman Arora ◽  
Aedin C. Culhane ◽  
Alexander V. Favorov ◽  
Lana X. Garmire ◽  
...  
Keyword(s):  
High Throughput ◽  
Matrix Factorization ◽  
Time Course ◽  
High Dimensional Data ◽  
Dimensional Structure ◽  
High Dimensional ◽  
Biological Knowledge ◽  
Omics Data ◽  
Cellular Interactions ◽  
Low Dimensional

AbstractOmics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge—answering questions from high-dimensional data that we have not yet thought to ask.

scholarly journals Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

2017 ◽  
Author(s):  
Genevieve Stein-O’Brien ◽  
Luciane T Kagohara ◽  
Sijia Li ◽  
Manjusha Thakar ◽  
Ruchira Ranaweera ◽  
...  
Keyword(s):  
Targeted Therapies ◽  
Time Course ◽  
Therapeutic Response ◽  
Acquired Resistance ◽  
New Drugs ◽  
Integrated Analysis ◽  
Omics Data ◽  
Epigenetic Alterations ◽  
Molecular Changes ◽  
Omics Analysis

AbstractBACKGROUNDTargeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients’ treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model.RESULTSAn unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.CONCLUSIONSGenes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

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