Abstract: The present disclosure discusses a system and method for disease module detection. More particularly, a protein network and list of seed proteins are provided to the system. The system iteratively selects one or more candidate proteins for inclusion in the list of seed proteins. The system calculates a connectivity factor for each of the connections of the candidate proteins to proteins listed as seed proteins. Responsive to the calculated connectivity factors the system adds one or more of the candidate proteins to list of seed proteins. At the end of the iterative process the list of seed proteins can be indicative of the disease module.

Abstract: The present technology relates to methods that determine one or more subgroups of subjects within a population of subjects diagnosed with the same disease. In some embodiments, the methods include determining differential gene expression of at least one subgroup in the population using divisive Shuffling Approach (VIStA). In some embodiments, the method includes determining at least one clinical characteristic of each subgroup and/or determining a significant set of clinical characteristics of the disease order.

Abstract: The disclosed methods and systems allow for a systematic quantification of the heterogeneity of disease states between different subjects on a molecular (e.g., gene or protein expression) level. One example embodiment of the invention is a method for determining a disease state of a patient. The method includes generating personalized biomarker expression perturbation profiles for a plurality of individual subjects with a disease. The profiles include representations of biomarker expressions that are perturbed beyond a threshold amount. The method also includes creating a disease module by combining representations of biomarkers from the personalized profiles. The disease module includes a network of representations of biomarkers having perturbations associated with the disease. The method also includes accessing biomarker data for the patient from a sample obtained from the patient and determining the disease state of the patient based on a comparison of the biomarker data and the disease module.

Abstract: The present disclosure discusses a system and method for disease module detection. More particularly, a protein network and list of seed proteins are provided to the system. The system iteratively selects one or more candidate proteins for inclusion in the list of seed proteins. The system calculates a connectivity factor for each of the connections of the candidate proteins to proteins listed as seed proteins. Responsive to the calculated connectivity factors the system adds one or more of the candidate proteins to list of seed proteins. At the end of the iterative process the list of seed proteins can be indicative of the disease module.

Abstract: Network-based relative proximity measures according to the present invention quantify the closeness between any two sets of nodes (e.g., drug targets and disease genes in a biological network, or groups of people in a social network). The proximity takes into account the scale-free nature of real-world networks and corrects for degree-bias (i.e., due to incompleteness or study biases) by incorporating various distance definitions between the two sets of nodes and comparison of these distances to those of randomly selected nodes in the network (i.e., the distance relative to random expectation), therefore improving processing of the network data. In brief, the proximity offers a formal framework to characterize the distance between two sets of nodes in the network with key applications in various domains from network pharmacology (e.g., discovering novel uses for existing drugs) to social sciences (e.g., defining similarity between groups of individuals).

Abstract: The present disclosure discusses a system and method for disease module detection. More particularly, a protein network and list of seed proteins are provided to the system. The system iteratively selects one or more candidate proteins for inclusion in the list of seed proteins. The system calculates a connectivity factor for each of the connections of the candidate proteins to proteins listed as seed proteins. Responsive to the calculated connectivity factors the system adds one or more of the candidate proteins to list of seed proteins. At the end of the iterative process the list of seed proteins can be indicative of the disease module.

Abstract: The present technology relates to methods that determine one or more subgroups of subjects within a population of subjects diagnosed with the same disease. In some embodiments, the methods include determining differential gene expression of at least one subgroup in the population using divisive Shuffling Approach (VIStA). In some embodiments, the method includes determining at least one clinical characteristic of each subgroup and/or determining a significant set of clinical characteristics of the disease order.