scholarly journals A Novel Method for Analyzing [Ca2+] Flux Kinetics in High-Throughput Screening

2006 ◽  
Vol 11 (5) ◽  
pp. 511-518 ◽  
Author(s):  
Philip Gribbon ◽  
Chris Chambers ◽  
Kaupo Palo ◽  
Juergen Kupper ◽  
Juergen Mueller ◽  
...  
Keyword(s):  
Time Series ◽  
High Throughput ◽  
High Throughput Screening ◽  
Time Series Data ◽  
Calcium Flux ◽  
Series Data ◽  
Imaging Plate ◽  
Additional Information ◽  
Novel Method ◽  
G Protein Coupled

Driven by multiparameter fluorescence readouts and the analysis of kinetic responses from biological assay systems, the amount and complexity of high-throughput screening data are constantly increasing. As a consequence, the reduction of data to a simple number, reflecting a percentage activity/inhibition, is no longer an adequate approach because valuable additional information, for example, about compound-or process-induced artifacts, is lost. Time series data such as the transient calcium flux observed after activation of Gq-coupled G protein-coupled receptors (GPCRs), are especially challenging with respect to quantity of data; typically, responses are followed for several minutes. Based on measurements taken on the fluorometric imaging plate reader, the authors have introduced a mathematical model to describe the time traces of cellular calcium fluxes mediated by the activation of GPCRs. The model describes the time series using 13 parameters, reducing the amount of data by 90% while guiding the detection of compound-induced artifacts as well as the selection of compounds for further characterization.

scholarly journals Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series

2018 ◽  
Author(s):  
Alexander M Crowell ◽  
Jennifer J. Loros ◽  
Jay C Dunlap
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
High Throughput ◽  
Reference Genes ◽  
Time Series Data ◽  
Prediction Interval ◽  
Series Data ◽  
Throughput Time ◽  
Additional Information ◽  
Ranking Score

AbstractMotivationIdentification of constitutive reference genes is critical for analysis of gene expression. Large numbers of high throughput time series expression data are available, but current methods for identifying invariant expression are not tailored for time series. Identification of reference genes from these data sets can benefit from methods which incorporate the additional information they provide.ResultsHere we show that we can improve identification of invariant expression from time series by modelling the time component of the data. We implement the Prediction Interval Ranking Score (PIRS) software, which screens high throughput time series data and provides a ranked list of reference candidates. We expect that PIRS will improve the quality of gene expression analysis by allowing researchers to identify the best reference genes for their system from publicly available time series.AvailabilityPIRS can be downloaded and installed with dependencies using ‘pip install pirs’ and Python code and documentation is available for download at https://github.com/aleccrowell/[email protected]

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

2010 ◽  
Vol 663 (1) ◽  
pp. 98-104 ◽  
Author(s):  
Sonja Peters ◽  
Hans-Gerd Janssen ◽  
Gabriel Vivó-Truyols
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Novel Method ◽  
Analysis Of Time Series

scholarly journals Identifying and Exploiting Diurnal Motifs in Wind Generation Time Series Data

2015 ◽  
Vol 29 (02) ◽  
pp. 1550012 ◽  
Author(s):  
Ya Ju Fan ◽  
Chandrika Kamath
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Historical Data ◽  
Wind Farms ◽  
Series Data ◽  
Wind Generation ◽  
Control Room ◽  
Additional Information ◽  
Diurnal Patterns ◽  
Using Data

Wind energy is scheduled on the power grid using 0–6 h ahead forecasts generated from computer simulations or historical data. When the forecasts are inaccurate, control room operators use their expertise, as well as the actual generation from previous days, to estimate the amount of energy to schedule. However, this is a challenge, and it would be useful for the operators to have additional information they can exploit to make better informed decisions. In this paper, we use techniques from time series analysis to determine if there are motifs, or frequently occurring diurnal patterns in wind generation data. We compare two different representations of the data and four different ways of identifying the number of motifs. Using data from wind farms in Tehachapi Pass and mid-Columbia Basin, we describe our findings and discuss how these motifs can be used to guide scheduling decisions.

scholarly journals Growth Score: a single metric to define growth in 96-well phenotype assays

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4681
Author(s):  
Daniel A. Cuevas ◽  
Robert A. Edwards
Keyword(s):  
Time Series ◽  
Systems Biology ◽  
High Throughput ◽  
Growth Curve ◽  
Time Series Data ◽  
Series Data ◽  
Direct Measurements ◽  
Essential Growth ◽  
Data Analytic ◽  
Require Data

High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including ourPMAnalyzerpipeline.

scholarly journals A METHOD OF TIME-SERIES CHANGE DETECTION USING FULL POLSAR IMAGES FROM DIFFERENT SENSORS

2018 ◽  
Vol XLII-3 ◽  
pp. 1127-1133
Author(s):  
W. Liu ◽  
J. Yang ◽  
J. Zhao ◽  
H. Shi ◽  
L. Yang
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Detection Methods ◽  
Series Data ◽  
Difference Image ◽  
Novel Method ◽  
Difference Images ◽  
The City

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by R<sub>j</sub> statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.

scholarly journals ­Growth Score: A single metric to define growth in 96-well phenotype assays

2018 ◽  
Author(s):  
Daniel A Cuevas ◽  
Robert A Edwards
Keyword(s):  
Time Series ◽  
Systems Biology ◽  
High Throughput ◽  
Growth Curve ◽  
Time Series Data ◽  
Series Data ◽  
Direct Measurements ◽  
Essential Growth ◽  
Data Analytic ◽  
Require Data

High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including our PMAnalyzer pipeline.

Sign in / Sign up

Export Citation Format

Share Document