<p><b>Copyright information:</b></p><p>Taken from "Portraits of breast cancer progression"</p><p>http://www.biomedcentral.com/1471-2105/8/291</p><p>BMC Bioinformatics 2007;8():291-291.</p><p>Published online 6 Aug 2007</p><p>PMCID:PMC1978212.</p><p></p> Red/green represent high/low fractional values across clustering methods and data perturbation replicates. The normals and the LG1 and LG2 are clearly well separated while the HG1, HG2, HG3 and HG4 separation is weaker. We find that the optimum number of clusters using gap-statistics oscillates between 6 and 7 with the HG3 and HG4 clusters merging at -6
<p>(a) A fraction of the ∼800 variants from Fig. 3 were randomly sampled and the resulting number of...
A novel neural network clustering algorithm, CoRe, is benchmarked against previously published resul...
<p>Adjacency matrices obtained by computing the normalized mutual information between observations o...
<p>The stability of the clusters increases with the number of clusters and stabilizes around four cl...
<p>A, consensus matrix at <i>k</i> = 4 for lncRNA expression across 63 UBCS samples. B, consensus ma...
101 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Clustering and classification...
<p>(A) Differences between the HC and PM groups in subjects' structural networks. The black solid po...
Additional file 10: Figure S3. Cluster evaluation of SC3 to determine the best cluster number. a). C...
<p>Left: The mean CD4 cell count (top) and frequency of ambiguous sites (bottom) versus the threshol...
Non-negative matrix factorization by maximizing correntropy for cancer clustering Jim Jing-Yan Wang1...
<p>A. Hierarchical clustering for the normalized data. Biological replicate samples cluster closely ...
<p>NMF consensus analysis of the merged data revealed a good consensus for k = 2. A. Maximum cophene...
<p>A) Average over ten restarts of the co-occurrence matrices for clustering with a fixed number of ...
<p><b>Copyright information:</b></p><p>Taken from "Masking repeats while clustering ESTs"</p><p>Nucl...
Figure S1. Evaluation of consensus clustering of the breast cancer dataset from k = 2 to k = 10. (A)...
<p>(a) A fraction of the ∼800 variants from Fig. 3 were randomly sampled and the resulting number of...
A novel neural network clustering algorithm, CoRe, is benchmarked against previously published resul...
<p>Adjacency matrices obtained by computing the normalized mutual information between observations o...
<p>The stability of the clusters increases with the number of clusters and stabilizes around four cl...
<p>A, consensus matrix at <i>k</i> = 4 for lncRNA expression across 63 UBCS samples. B, consensus ma...
101 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Clustering and classification...
<p>(A) Differences between the HC and PM groups in subjects' structural networks. The black solid po...
Additional file 10: Figure S3. Cluster evaluation of SC3 to determine the best cluster number. a). C...
<p>Left: The mean CD4 cell count (top) and frequency of ambiguous sites (bottom) versus the threshol...
Non-negative matrix factorization by maximizing correntropy for cancer clustering Jim Jing-Yan Wang1...
<p>A. Hierarchical clustering for the normalized data. Biological replicate samples cluster closely ...
<p>NMF consensus analysis of the merged data revealed a good consensus for k = 2. A. Maximum cophene...
<p>A) Average over ten restarts of the co-occurrence matrices for clustering with a fixed number of ...
<p><b>Copyright information:</b></p><p>Taken from "Masking repeats while clustering ESTs"</p><p>Nucl...
Figure S1. Evaluation of consensus clustering of the breast cancer dataset from k = 2 to k = 10. (A)...
<p>(a) A fraction of the ∼800 variants from Fig. 3 were randomly sampled and the resulting number of...
A novel neural network clustering algorithm, CoRe, is benchmarked against previously published resul...
<p>Adjacency matrices obtained by computing the normalized mutual information between observations o...