Pattern Recognition Letters
Spectral analysis of microarray gene expression time series data of Plasmodium falciparum
International Journal of Bioinformatics Research and Applications
Label ranking by learning pairwise preferences
Artificial Intelligence
Efficient multi-class cancer diagnosis algorithm, using a global similarity pattern
Computational Statistics & Data Analysis
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data
Similarity-Based Clustering
Clustering of gene expression data based on shape similarity
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Expert Systems with Applications: An International Journal
Cross-Correlation and Evolutionary Biclustering: Extracting Gene Interaction Sub-networks
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Evolutionary biclustering with correlation for gene interaction networks
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
Independent component analysis: Mining microarray data for fundamental human gene expression modules
Journal of Biomedical Informatics
Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering
International Journal of Data Mining and Bioinformatics
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Parametric spectral analysis of malaria gene expression time series data
CompLife'06 Proceedings of the Second international conference on Computational Life Sciences
A formal and empirical analysis of the fuzzy gamma rank correlation coefficient
Information Sciences: an International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Motivation: Microarray technology enables the study of gene expression in large scale. The application of methods for data analysis then allows for grouping genes that show a similar expression profile and that are thus likely to be co-regulated. A relationship among genes at the biological level often presents itself by locally similar and potentially time-shifted patterns in their expression profiles. Results: Here, we propose a new method (CLARITY; Clustering with Local shApe-based similaRITY) for the analysis of microarray time course experiments that uses a local shape-based similarity measure based on Spearman rank correlation. This measure does not require a normalization of the expression data and is comparably robust towards noise. It is also able to detect similar and even time-shifted sub-profiles. To this end, we implemented an approach motivated by the BLAST algorithm for sequence alignment. We used CLARITY to cluster the times series of gene expression data during the mitotic cell cycle of the yeast Saccharomyces cerevisiae. The obtained clusters were related to the MIPS functional classification to assess their biological significance. We found that several clusters were significantly enriched with genes that share similar or related functions. Contact:kaemper@staff.uni-marburg.de; eyke@mathematik.uni-marburg.de Availability: Upon request from the authors.