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SurvOmics: a multi-omics database for cancer survival analysis
Example : EGFR
Example : EGFR
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The Summary section provides visualizations to illustrate the survival probability and interaction of a user-selected gene identified in multi-omics levels across multiple cancer types.
Survival map
The heatmap provides the distribution of the survival impact of a user-selected gene in 4-omics levels across multiple cancer types.
Click on a specific color space, and the corresponding KM plot will pop up. (The machine learning results will be provided in a new tab)
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Survival network
The synergistic effect of a user-selected gene and its related genes.
Synergistic survival analysis
Kaplan-Meier curves for a user-selected gene and the related genes in different omics, derived from synergistic effect analysis.
Select the radio button to show KM plot.
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The RNA section provides visualizations to illustrate the survival probability of a user-selected gene across multiple cancer types, based on RNA expression computed by multiple algorithms.
Survival map
The heatmap provides the distribution of the survival impact of a user-selected gene. The color spaces represent significance according to log2 hazard ratios and p-value.
Click on a specific color space, and the corresponding KM plot will pop up. (The machine learning results will be provided in a new tab)
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Survival analysis
Kaplan-Meier curves for a user-selected gene derived from univariate cox proportional regression analysis (cox uni).
Click on a mini plot below to view the results of a specific survival time, survival type, and stratify type.
Cancer type :
Best All-time
Median All-time
Best
5-year
Median 5-year
OS
PFI
DFI
DSS
Survival analysis
Adjustive curves for a user-selected gene derived from multiple cox proportional regression analysis
with clinical variables (cox multi (clinical)).
Click on a mini plot below to view the results of a specific survival time, survival type, and stratify type.
Cancer type :
Best All-time
Median All-time
Best
5-year
Median 5-year
OS
PFI
DFI
DSS
Survival analysis
Predictive curves for a user-selected gene derived from cure model.
Cancer type :
Median All-time
Survival analysis
Kaplan-Meier curves for a user-selected gene derived from machine learning algorithms (Lasso, Random forest, and I-Boost).
Click on the tabs below to view the results of different machine-learning algorithms.
Lasso
Signature selection
Genes in signature
Kaplan-Meier plot
ROC curve
Random forest
Signature selection
Genes in signature
Kaplan-Meier plot
ROC curve
iboost
Signature selection
Genes in signature
Kaplan-Meier plot
Cumulative hazard ratio
Synergistic survival analysis
Kaplan-Meier curves for a user-selected gene and the related genes in different omics, derived from synergistic effect analysis.
Select the radio button to show KM plot.
The CNV section provides visualizations to illustrate the survival probability of a user-selected gene across multiple cancer types, based on copy number variation computed by multiple algorithms.
Survival map
The heatmap provides the distribution of the survival impact of a user-selected gene. The color spaces represent significance according to log2 hazard ratios and p-value.
Click on a specific color space, and the corresponding KM plot will pop up. (The machine learning results will be provided in a new tab)
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Survival analysis
Kaplan-Meier curves for a user-selected gene derived from univariate cox proportional regression analysis (cox uni).
Click on a mini plot below to view the results of a specific survival time, survival type, and definition.
Cancer type :
iGC
All-time
Gistic All-time
iGC
5-year
Gistic 5-year
OS
PFI
DFI
DSS
Survival analysis
Adjustive curves for a user-selected gene derived from multiple cox proportional regression analysis
with clinical variables (cox multi (clinical)).
Click on a mini plot below to view the results of a specific survival time, survival type, and definition.
Cancer type :
iGC
All-time
Gistic All-time
iGC
5-year
Gistic 5-year
OS
PFI
DFI
DSS
Survival analysis
Predictive curves for a user-selected gene derived from cure model.
Cancer type :
iGC All-time
Gistic All-time
Survival analysis
Kaplan-Meier curves for a user-selected gene derived from machine learning algorithms (Lasso, Random forest, and I-Boost).
Click on the tabs below to view the results of different machine-learning algorithms.
Lasso
Signature selection
Genes in signature
Kaplan-Meier plot
ROC curve
Random forest
Signature selection
Genes in signature
Kaplan-Meier plot
ROC curve
iboost
Signature selection
Genes in signature
Kaplan-Meier plot
Cumulative hazard ratio
Synergistic survival analysis
Kaplan-Meier curves for a user-selected gene and the related genes in different omics, derived from synergistic effect analysis.
Select the radio button to show KM plot.
The Mutation section provides visualizations to illustrate the survival probability of a user-selected gene across multiple cancer types, based on mutation computed by multiple algorithms.
Survival map
The heatmap provides the distribution of the survival impact of a user-selected gene. The color spaces represent significance according to log2 hazard ratios and p-value.
Click on a specific color space, and the corresponding KM plot will pop up. (The machine learning results will be provided in a new tab)
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Survival analysis
Kaplan-Meier curves for a user-selected gene derived from univariate cox proportional regression analysis (cox uni).
Click on a mini plot below to view the results of a specific survival time and survival type.
Cancer type :
All-time
5-year
OS
PFI
DFI
DSS
Survival analysis
Adjustive curves for a user-selected gene derived from multiple cox proportional regression analysis
with clinical variables (cox multi (clinical)).
Click on a mini plot below to view the results of a specific survival time and survival type.
Cancer type :
All-time
5-year
OS
PFI
DFI
DSS
Survival analysis
Predictive curves for a user-selected gene derived from cure model.
Cancer type :
All-time
Survival analysis
Kaplan-Meier curves for a user-selected gene derived from machine learning algorithms (Lasso, Random forest, and I-Boost).
Click on the tabs below to view the results of different machine-learning algorithms.
Lasso
Signature selection
Genes in signature
Kaplan-Meier plot
ROC curve
Random forest
Signature selection
Genes in signature
Kaplan-Meier plot
ROC curve
iboost
Signature selection
Genes in signature
Kaplan-Meier plot
Cumulative hazard ratio
Synergistic survival analysis
Kaplan-Meier curves for a user-selected gene and the related genes in different omics, derived from synergistic effect analysis.
Select the radio button to show KM plot.
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The Methylation section provides visualizations to illustrate the survival probability of a user-selected gene across multiple cancer types, based on DNA methylation computed by multiple algorithms.
Survival map
The heatmap provides the distribution of the survival impact of a user-selected gene. The color spaces represent significance according to log2 hazard ratios and p-value.
Click on a specific color space, and the corresponding KM plot will pop up. (The machine learning results will be provided in a new tab)
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