State-feedback control of Partially-Observed Boolean Dynamical Systems using RNA-seq time series data

2016 ◽  
Author(s):  
Mahdi Imani ◽  
Ulisses Braga-Neto
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Feedback Control ◽  
State Feedback ◽  
Time Series Data ◽  
State Feedback Control ◽  
Series Data ◽  
Rna Seq ◽  
Partially Observed

scholarly journals Identification and visualisation of differential isoform expression in RNA-seq time series

2017 ◽  
Author(s):  
María José Nueda ◽  
Jordi Martorell-Marugan ◽  
Cristina Martí ◽  
Sonia Tarazona ◽  
Ana Conesa
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Expression Patterns ◽  
R Package ◽  
Series Data ◽  
Rna Seq ◽  
Sequencing Technologies ◽  
Isoform Expression ◽  
Time Series Data Analysis ◽  
Differential Isoform Expression

AbstractAs sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. The package is freely available under the LGPL license from the Bioconductor web site (http://bioconductor.org).

scholarly journals Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis

2020 ◽  
Author(s):  
Robert Glenn Moulder ◽  
Elena Martynova ◽  
Steven M. Boker
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Time Series Data ◽  
Nonlinear Dynamical Systems ◽  
Series Data ◽  
Alternative View ◽  
Unified Framework ◽  
Nonlinear Dynamical ◽  
Depth Analysis ◽  
Analysis Of Time Series

Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies.

scholarly journals Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks

2021 ◽  
Author(s):  
Sibo Cheng ◽  
Mingming Qiu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Dynamical Systems ◽  
Data Assimilation ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Observation Data ◽  
Observation Error ◽  
Error Covariance

AbstractData assimilation techniques are widely used to predict complex dynamical systems with uncertainties, based on time-series observation data. Error covariance matrices modeling is an important element in data assimilation algorithms which can considerably impact the forecasting accuracy. The estimation of these covariances, which usually relies on empirical assumptions and physical constraints, is often imprecise and computationally expensive, especially for systems of large dimensions. In this work, we propose a data-driven approach based on long short term memory (LSTM) recurrent neural networks (RNN) to improve both the accuracy and the efficiency of observation covariance specification in data assimilation for dynamical systems. Learning the covariance matrix from observed/simulated time-series data, the proposed approach does not require any knowledge or assumption about prior error distribution, unlike classical posterior tuning methods. We have compared the novel approach with two state-of-the-art covariance tuning algorithms, namely DI01 and D05, first in a Lorenz dynamical system and then in a 2D shallow water twin experiments framework with different covariance parameterization using ensemble assimilation. This novel method shows significant advantages in observation covariance specification, assimilation accuracy, and computational efficiency.

scholarly journals Statistical modeling of isoform splicing dynamics from RNA-seq time series data

2016 ◽  
Vol 32 (19) ◽  
pp. 2965-2972 ◽  
Author(s):  
Yuanhua Huang ◽  
Guido Sanguinetti
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Time Series Data ◽  
Series Data ◽  
Rna Seq

scholarly journals Similarity Preserving Representation Learning for Time Series Clustering

2019 ◽  
Author(s):  
Qi Lei ◽  
Jinfeng Yi ◽  
Roman Vaculin ◽  
Lingfei Wu ◽  
Inderjit S. Dhillon
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Clustering Algorithms ◽  
Representation Learning ◽  
Feature Representation ◽  
Series Data ◽  
Similarity Matrix ◽  
Clustering Methods ◽  
Time Series Clustering ◽  
Partially Observed

A considerable amount of clustering algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with various lengths to an instance-feature matrix. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation , thus the learned feature representation is particularly suitable for the time series clustering task. Given a set of $n$ time series, we first construct an $n\times n$ partially-observed similarity matrix by randomly sampling $\mathcal{O}(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an efficient algorithm that solves a non-convex and NP-hard problem to learn new features based on the partially-observed similarity matrix. By conducting extensive empirical studies, we demonstrate that the proposed framework is much more effective, efficient, and flexible compared to other state-of-the-art clustering methods.

SINGLE STATE-FEEDBACK CHAOTIFICATION OF DISCRETE DYNAMICAL SYSTEMS

2004 ◽  
Vol 14 (01) ◽  
pp. 279-284 ◽  
Author(s):  
YONGAI ZHENG ◽  
GUANRONG CHEN
Keyword(s):  
Control System ◽  
Dynamical Systems ◽  
Feedback Control ◽  
Nonlinear System ◽  
State Feedback ◽  
Discrete Dynamical Systems ◽  
State Feedback Control ◽  
Sine Function ◽  
System State ◽  
Single State

In this paper, the problem of making a nonlinear system chaotic by using state-feedback control is studied, where the feedback controller uses a simple sine function of the system state with only one single component in each dimension. It is proved that the designed control system generates chaos in the sense of Li and Yorke.

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