Our tool supports two types of data exploration query-by-example and clustexploration query-by-example
We consider several use cases for both query-by-example and clustering scenarios, and show that AnnotCompute enriches the user experience, particularly for well-annotated experiments.
For example, batch and cluster effects [ 9, 10] always occur in the experiments and sometimes they may be the major cause of incorrectly estimated FDR.
Since many traditional network properties for example, clustering coefficient and assortativity are developed largely based on CND, we may then define CSD-based versions of such network properties.
For example, clustering the active and moderately-active compounds from the NCI HIV screen using the Quality Threshold clustering algorithm [28], the Tanimoto distance, and 0.5 as the cluster diameter parameter, produces a few large clusters and many smaller clusters (see Figure 8).
DTW is widely used, for example, for classification and clustering tasks in econometrics, chemometrics, and general time series mining (Giorgino 2015).
The data also provide biological insights into which cell characteristics (proliferation and adhesion, for example) are functionally clustered and either change or remain constant with aging in vitro.
The goal is to demonstrate the CRV value computation to show that for the same row ex1, we can get different (mathrm {CRV}_{ex1}) values depending on which example ex1 is clustered with, and finally to show how (mathrm {CRV}_{ex1}) values decrease when similar examples are added into cluster C. If ex1 is the only example in a cluster (C={ex1}): (mathrm {CRV}_{ex1,{text{wc}}} =0).
The majority of such approaches evaluate interactome topological features, and typical examples are node degree and clustering coefficient, both based on the levels of connectivity of each node.
Several approaches have been suggested to overcome these problems, for example, iterative clustering and iterative maximization of the partition quality [ 5].
Metabolomics data from breast cancer patients can be used in a similar way to gene expression microarray data, using, for example, hierarchical clustering and heat maps.
