Index of papers in April 2015 that mention
  • time series
Stuart Aitken, Shigeyuki Magi, Ahmad M. N. Alhendi, Masayoshi Itoh, Hideya Kawaji, Timo Lassmann, Carsten O. Daub, Erik Arner, Piero Carninci, Alistair R. R. Forrest, Yoshihide Hayashizaki, Levon M. Khachigian, Mariko Okada-Hatakeyama, Colin A. Semple , the FANTOM Consortium
Abstract
Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli.
Abstract
We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities overtime.
Abstract
Using a novel analysis method for time series data we identify transcripts with expression patterns that closely resemble the dynamics of known immediate-early genes (IEGs) and this enables a comprehensive comparative study of these genes and their chromatin state.
Core regulatory components in the immediate-early response
The percentage of CAGE time series that could be reliably annotated with a kinetic signature varied from 21%—39% across the cell types and protein coding or RNA biotypes.
Introduction
In particular, the CAGE time series datasets obtained for MCF-7 cells and human primary aortic smooth muscle cells are a unique resource for the study of the temporal response of stimulated human cells [19].
Introduction
Using these unique datasets, and a novel approach to time series analysis, we identify a comprehensive set of transcripts whose expression patterns are altered in response to a stimulus genome-wide, including all ncRNA transcripts present.
Results
We focused on four particular time series datasets: human aortic smooth muscle cells (AoSMC) treated with FGF2 and with IL-lfi (9 time points from 0 to 360 min; 3 replicates per treatment; IL-lfi will be referred to as Ile hereafter), as well as human MCF7 breast cancer cells treated with EGF and HRG (16 time points from 0 to 480 min; 3 replicates per treatment).
Results
This led to exclusion of five libraries from the AoSMC-FGF2 time series , three from AoSMC-IL1b and one from MCF7-EGF.
Results
For the analysis of time series in this context, we distinguished early peaks from late peaks by bounding the prior range for the tS parameter by 1-240 minutes (the first half of the time series ), and by 240 minutes-end time (the second half) of the experiment respectively.
time series is mentioned in 19 sentences in this paper.
Topics mentioned in this paper:
Tom Lindström, Michael Tildesley, Colleen Webb
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For climate considerations, we envisage that the precision of natural variability, To, would be large relative to each 2,- if bias is assessed by comparing model simulations to long time series of climate data.
Introduction
Yet, at courser resolution climate researchers have access to long time series of climate variables to assess model bias.
Introduction
However, for emerging diseases, long time series would rarely be available, making the lack of data an even bigger issue for epidemiology.
time series is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Joon-Young Moon, UnCheol Lee, Stefanie Blain-Moraes, George A. Mashour
Coupled Stuart-Landau/Kuramoto model parameters
Model time series for the measurement
Coupled Stuart-Landau/Kuramoto model parameters
With each model, we produce a times series of length 10,000 for each run of the simulation, and then take the latter half of the time series for the measurement.
Coupled Stuart-Landau/Kuramoto model parameters
The sampling rate of the time series is 1,000HZ, making the length of the produced time series 10s containing approximately 100 cycles of oscillation.
time series is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: