Abstract: Provided are methods and devices for mutation prioritization, which are helpful in personalized therapy of a patient. Also, provided are methods and devices for generating a disease knowledgebase. Information present in various categories of knowledge sources with respect to a particular association of <Disease, Gene, Mutation> set may be identified. The identified information is ranked with respect to the disease knowledgebase to find out the most relevant ones for the treatment of a particular Disease/Gene/Mutation of a patient, thereby enabling medical experts to personalize a therapy to be administered to a patient.

Abstract: Methods and devices for clustering a plurality of sub-networks of a larger interaction network using an enhanced hierarchical clustering algorithm are disclosed. The methods provide expression based sub-network generation using differentially expressed markers. The enhanced hierarchical clustering algorithm clusters the generated sub-networks based on a user defined customizable similarity coefficient. The methods use non-Boolean links to cluster similar sub-networks. This provides consideration of indirect relationships among sub-networks. The customizable similarity coefficient enables the methods to be used for diverse applications such as biomarker detection, patient stratification, personalized therapy, drug efficacy prediction, genetic similarity analysis in genetic diseases. The methods enable patient grouping based on the enhanced hierarchical clustering algorithm.

Abstract: Provided are methods and devices for mutation prioritization, which are helpful in personalized therapy of a patient. Also, provided are methods and devices for generating a disease knowledgebase. Information present in various categories of knowledge sources with respect to a particular association of <Disease, Gene, Mutation> set may be identified. The identified information is ranked with respect to the disease knowledgebase to find out the most relevant ones for the treatment of a particular Disease/Gene/Mutation of a patient, thereby enabling medical experts to personalize a therapy to be administered to a patient.

Abstract: Methods and devices for clustering a plurality of sub-networks of a larger interaction network using an enhanced hierarchical clustering algorithm are disclosed. The methods provide expression based sub-network generation using differentially expressed markers. The enhanced hierarchical clustering algorithm clusters the generated sub-networks based on a user defined customizable similarity coefficient. The methods use non-Boolean links to cluster similar sub-networks. This provides consideration of indirect relationships among sub-networks. The customizable similarity coefficient enables the methods to be used for diverse applications such as biomarker detection, patient stratification, personalized therapy, drug efficacy prediction, genetic similarity analysis in genetic diseases. The methods enable patient grouping based on the enhanced hierarchical clustering algorithm.

Abstract: The present invention provides a method and system for managing various Regulations, Policies and Risks (RPR). These RPR are managed for supporting Governance, Risk and Compliance requirements of an organization. The method includes defining a tree structure for a context. The tree structure is defined for one or more RPR and one or more roles of a plurality of users of the organization. The RPR are intervened across various trees. A plurality of nodes is defined for the tree. Each node includes a plurality of attributes and one or more attribute functions.

Abstract: The present invention provides a method and system for managing various Regulations, Policies and Risks (RPR). These RPR are managed for supporting Governance, Risk and Compliance requirements of an organization. The method includes defining a tree structure for a context. The tree structure is defined for one or more RPR and one or more roles of a plurality of users of the organization. The RPR are intervened across various trees. A plurality of nodes is defined for the tree. Each node includes a plurality of attributes and one or more attribute functions.