[PDF] Top 20 Improved robustness in time series analysis of gene expression data by polynomial model based clustering
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Improved robustness in time series analysis of gene expression data by polynomial model based clustering
... direct clustering (DC) of the data with standard methods [5] becomes less ...the data has considerable missing data, the straightforward calculation of the score functions homogeneity and ... See full document
11
A Complementary Review of Data-based Clustering Model and Data Analysis for Gene Expressions
... get time-series expression knowledge for learning a large vary of biological ...the expression knowledge tends to contain respectable noise that as a result might deteriorate the ... See full document
8
An integrated semi supervised clustering model for time course gene expression data
... Recently gene set analysis (TCGSA) has been proposed to cluster the predefined groups of genes in the analysis of gene expression data in cross-sectional studies ...of ... See full document
7
Sales Prediction : Analysis of Time Series Data Using K-Means Based Smooth Subspace Clustering
... main time series data to evaluation the model parameters, so this model to forecast sales time ...Earlier time period to launch the sales time series of ... See full document
7
Partial mixture model for tight clustering of gene expression time course
... Various model-based methods have been proposed to accommodate the needs for data mining in such massive ...these model-based methods is to fit a finite mixture model to the ... See full document
18
A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series
... Time series gene expression data, obtained from microar- ray experiments performed in successive instants of time, can be used to study a wide range of biological problems [1], ... See full document
39
A Link-Based Cluster Ensemble Approach For Improved Gene Expression Data Analysis
... optimal clustering results based on the incomplete information of the cluster ...the improved cluster association matrix, instead of conventional ...of clustering cancer microarray samples, ... See full document
6
Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell
... A clustering algorithm is used to group genes that are coexpressed over all conditions/samples or to group experimental conditions over all genes based on some similarity/dissimilarity ...However ... See full document
11
Fuzzy clustering of time series gene expression data with cubic spline
... biological data has been extracted from microarrays. Analysis of these data on the molecular level is revolutionary in medicine be- cause they are highly ...methodologies. Clustering of ... See full document
6
Clustering Time Series Gene Expression Data Based on Sum-of-Exponentials Fitting
... the time series col- lected at nonuniform time points has been presented in [10], where the sum-of-exponentials model was used for getting estimates of the gains and time constants, and ... See full document
15
An Adaptive Clustering Algorithm for Gene Expression Time-Series Data Analysis
... our time-series ...The time intervals are chosen from anywhere in the dataset (beginning, middle or end) without changing the order of ...end based on step ...similar expression trend ... See full document
91
A temporal precedence based clustering method for gene expression microarray data
... the data to the unknown model that describes the ...are based on statistical mixture models which assume that data is generated by a finite mixture of underlying probability distributions, ... See full document
26
BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data
... and robustness. Clustering techniques have been extensively applied to both dimen- sions of expression matrices separately, focusing on either gene or sample expression ...local ... See full document
11
Clustering Inverse: A Generalized Model for Pattern Based Time Series Segmentation
... a time series. Especially, a histogram time series usually contains the following rep- resentative technique patterns: peak and ...histogram time series segment which appears ... See full document
11
Semi-supervised consensus clustering for gene expression data analysis
... final clustering. The consensus clustering algorithms differ in chosen algo- rithms for basic clustering, consensus function and final ...hierarchical clustering(HC) or self-organizing map ... See full document
13
Comparing Clustering Algorithms using Financial Time-series data
... Time-series data is a kind of data that was collected from the data ...of time such as daily stock data, daily currency exchanged rate, daily temperature data or ... See full document
21
Time series clustering in large data sets
... compare clustering results made with diff erent parameters of feature vectors and the SOM ...describing time series in a simplistic way evaluating stan- dard deviations for separated parts of ... See full document
6
Clustering of Leukemia Patients via Gene Expression Data Analysis
... patient clustering, it is possible to apply a filter to remove those genes of little ...of time and storage is inevitable, and a large amount of computational resources is required as ...of analysis ... See full document
63
Anomaly behaviour detection based on the meta-Morisita index for large scale spatio-temporal data set
... Applications based on RANdomized Search) [29], K-modes [30], ISODATA (Iterative Self-Organizing Data Analysis Technique) [31], FCM (fuzzy-c-means) [32] is scalability for large data and ... See full document
28
A robust approach based on Weibull distribution for clustering gene expression data
... approach based on Weibull distribution (WDCM) for clustering gene expression ...is based on the idea that a group of genes tend to be clustered together if the distributions of ... See full document
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