<p>A shows the survival time curves of three classes obtained by IGSA (p value of 0.0362). B shows the survival analysis of three classes obtained by HCBP (p value of 0.187). C shows the survival time curves of three classes obtained by HCBG (p value of only 0.240). D shows the survival time curves of two classes (class 1 and class 2,3) obtained by IGSA (p value of 0.0362). The p values in both A and D are significant compared with HCBP and HCBG.</p
Comparison of gene expression of TCGA ovarian cancer sub-types by patient samples (top) and cell clu...
Multidimensional scaling (MDS) plots showing dissimilarity of patients based on their gene expressio...
Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the progn...
<p>Part A shows the survival time curves of two classes obtained by IGSA (p value of 0.0778). Part B...
<p>Breast cancer and ovarian cancer molecular subtypes were clustered with the 1300 gene sets with a...
<p>Each row and column means an individual gene and an individual patient sample, respectively. Afte...
<p><b>A</b>) Unsupervised hierarchical clustering of raw log2 ratios derived from 72 serous type ova...
<p>(a) and (b). Comparison of Kaplan-Meier survival plots based on the unsupervised clusters of Hier...
<p>Hierarchical cluster analysis of 174-marker antibody arrays in ovarian cancers and healthy contro...
<p>The ISIS algorithm identified four independent binary partition classifications (splits) of 129 o...
<p>The differences between the survival curves are significant with <i>p</i> = 0.0382 using the log-...
Kaplan-Meier plots compare the associations of molecular subtypes of ovarian cancer identified using...
<div><p>(A) Unsupervised hierarchical clustering of breast cancer patients (columns) obtained using ...
Hierarchical Clustering for timepoint 3 (Numbers refer to patients as per Table 1) (a) and relative ...
<p>Hierarchical clustering of 183 breast tumor samples using the 500 most variant genes across all s...
Comparison of gene expression of TCGA ovarian cancer sub-types by patient samples (top) and cell clu...
Multidimensional scaling (MDS) plots showing dissimilarity of patients based on their gene expressio...
Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the progn...
<p>Part A shows the survival time curves of two classes obtained by IGSA (p value of 0.0778). Part B...
<p>Breast cancer and ovarian cancer molecular subtypes were clustered with the 1300 gene sets with a...
<p>Each row and column means an individual gene and an individual patient sample, respectively. Afte...
<p><b>A</b>) Unsupervised hierarchical clustering of raw log2 ratios derived from 72 serous type ova...
<p>(a) and (b). Comparison of Kaplan-Meier survival plots based on the unsupervised clusters of Hier...
<p>Hierarchical cluster analysis of 174-marker antibody arrays in ovarian cancers and healthy contro...
<p>The ISIS algorithm identified four independent binary partition classifications (splits) of 129 o...
<p>The differences between the survival curves are significant with <i>p</i> = 0.0382 using the log-...
Kaplan-Meier plots compare the associations of molecular subtypes of ovarian cancer identified using...
<div><p>(A) Unsupervised hierarchical clustering of breast cancer patients (columns) obtained using ...
Hierarchical Clustering for timepoint 3 (Numbers refer to patients as per Table 1) (a) and relative ...
<p>Hierarchical clustering of 183 breast tumor samples using the 500 most variant genes across all s...
Comparison of gene expression of TCGA ovarian cancer sub-types by patient samples (top) and cell clu...
Multidimensional scaling (MDS) plots showing dissimilarity of patients based on their gene expressio...
Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the progn...