In data mining, cluster analysis is used to classify a set of observations into two or more mutually exclusive unknown groups, based on combinations of the interval variables. The purpose is to discover a system of organizing observations, usually genes, and proteins into groups, where members of the groups share properties in common. In Creative Proteomics, we can interpret the data you collected with a set of typical clustering methodologies, algorithms, and applications, which include partitioning methods such as k-means, hierarchical methods and density-based methods. Your data can be interpreted and visualized with our assistance.
Clustering analysis generally consists of the following steps:
- Finding appropriate dissimilarity or similarity measure
- Deciding on the grouping algorithm
- Choosing the appropriate number of clusters
- Describing and profiling the newly-formed clusters
Applications in the field of computational biology for clustering analysis:
- Genetic clustering
- Transcriptomics
- Proteomics
- Sequence analysis
- High-throughput genotyping platforms
- Medical imaging
- Analysis of antimicrobial activity
Clustering analysis services provided by Creative Proteomics include:
- Hierarchical clustering
- K-means clustering
- STEM analysis
- Other clustering methods if you need!
How to place an order:
*If your organization requires signing of a confidentiality agreement, please contact us by email
As one of the leading omics industry company in the world! Creative Proteomics now is opening to provide clustering analysis service for our customers. With rich experience in the field of bioinformatics, we are willing to provide our customer the most outstanding service! Contact us for all the detailed informations!
