Time-series microarray data simulation modeled with a case-control label

Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

Exploring Microarray Time Series with Protégé

Data mining of microarray data more often includes exploration of temporal data. E.g., researchers might look for temporal changes in gene expression to determine responses to an injury. Knowledge-based Temporal Abstraction does not assume that the pattern of time points is similar to the original experimental design. We extended this framework by creating an software tool using open-source pla...

Clustering Algorithms for Time Series Gene Expression in Microarray Data

illustrations, 75 numbered references. Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements on an existing profile-based greedy algorithm for short time-series data; the first one is implementation of a scaling method on the pre-processing of the ...

cDNA Microarray Data Normalization

Detecting Gene Regulation Relations from Microarray Time Series Data

Microarrays are important tools in the quest to map the gene regulation networks of cells. A common use of microarrays result in time series pairs that indicates how the output of one gene affects another. Substantial efforts have been made towards identifying pairs of microarray time series that indicate that one gene is a regulator for another. However, most approaches make assumptions about ...