Computational Statistics & Data Analysis
Use of SVD-based probit transformation in clustering gene expression profiles
Computational Statistics & Data Analysis
Classification of gene functions using support vector machine for time-course gene expression data
Computational Statistics & Data Analysis
Weighted rank aggregation of cluster validation measures
Bioinformatics
Assessing agreement of clustering methods with gene expression microarray data
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
Which Distance for the Identification and the Differentiation of Cell-Cycle Expressed Genes?
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Non-linear time series clustering based on non-parametric forecast densities
Computational Statistics & Data Analysis
Time series labeling algorithms based on the K-nearest neighbors' frequencies
Expert Systems with Applications: An International Journal
Classification trees for time series
Pattern Recognition
Phase and amplitude-based clustering for functional data
Computational Statistics & Data Analysis
A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples
Computational Statistics & Data Analysis
Polarization of forecast densities: A new approach to time series classification
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
The biological problem of identifying the active genes during the cell division process is addressed. The cell division ensures the proliferation of cells, which is drastically aberrant in cancer cells. The studied genes are described by their expression profiles during the cell division cycle. Commonly, the identification process is a supervised approach based on an a priori set of reference genes, assumed as well-characterizing the cell cycle phases. Each studied gene is then classified by its peak similarity to one pre-specified reference gene. This classical approach suffers from two limitations. On the one hand, there is no consensus between biologists about the set of reference genes to consider for the identification process. On the other hand, the proximity measures used for genes expression profiles are unjustified and mainly based on the expression values regardless of the genes expression behavior. To identify genes expression profiles, a new adaptive clustering approach is proposed which consists of two main points. First, it allows in an unsupervised way the selection of a well-justified set of reference genes, to be compared with the pre-specified ones. Secondly, it enables the users to learn the appropriate proximity measure to use for genes expression data, a measure which will cover both proximity on values and on behavior. The adaptive clustering method is compared to a correlation-based approach through public and simulated genes expression data.