Abstract: Scores for particular network models (those having a source node connected to a set of measurable downstream nodes via causal edges) are computed across multiple networks by accounting for an overlap between these models in a manner that reduces cross-network redundancy and increases the specificity of the network models for the network in which they are found. According to another aspect, a meta-network model is created for networks by accounting for the occurrence of network models that are found in multiple networks in a manner that reduces the redundancy across networks and that increases the specificity of the network model score. Preferably, this process provides additional weighting factors for each node in the network model.

Abstract: Described are methods, systems and apparatus for hypothesizing a biological relationship in a biological system. A database of biological assertions is provided consisting of biological elements, relationships among the biological elements, and relationship descriptors characterizing the properties of the elements and relationships. A biological element may be selected from the database and a logical simulation may be performed within the biological database, from the selected biological element, through relationship descriptors, along a path defined by potentially causative biological elements to discern a biological element hypothetically responsible for the change in the selected biological element.

Abstract: A “Specificity” statistic (or metric) is computed as a means to identify amplitude scores associated with a signature that can be attributed with high probability to a specific biological entity or process represented by the signature. Preferably, Specificity is computed by assessing a likelihood of a given null hypothesis, namely, that an amplitude score is not representative of the specific signature but, instead, is representative of a general trend in the applicable data set that can be measured by any signature that is comparable to the signature of interest. In a typical implementation, a first step to compute the Specificity metric is to construct a set of comparable signatures. Next, an amplitude score is computed for each of these signatures, preferably using the same data set. Then, the Specificity metric is computed, preferably as a two-tailed p-value, by placing the amplitude score for the signature of interest on a distribution of scores for the comparable signatures.

Abstract: To directly compare two or more network perturbation amplitude scores and identify whether the difference between them is meaningful, an Uncertainty (confidence interval) for each of the scores is computed. According to this disclosure, experimental replicates of the measurements are used to compute the score Uncertainty, based on an assumption that variability between measurement replicates represents a largest source of error in the score. Preferably, at least three (3) experimental replicates for both treated and control conditions are used to compute Uncertainty.

Abstract: One or more measurement signatures are derived from a knowledge base of casual biological facts, where a signature is a collection of measured node entities and their expected directions of change with respect to a reference node. The knowledge base may be a directed network of experimentally-observed casual relationships among biological entities and processes, and a reference node represents a perturbation. A degree of activation of a signature is then assessed by scoring one or more “differential” data sets against the signature to compute an amplitude score. The amplitude score quantifies fold-changes of measurements in the signature. In one particular embodiment, the amplitude score is a weighted average of adjusted log-fold changes of measured node entities in the signature, wherein an adjustment applied to the log-fold changes is based on their expected direction of change. In an alternative embodiment, the amplitude score is based on quantity effects.

Abstract: Described are methods, systems and apparatus for hypothesizing a biological relationship in a biological system. A database of biological assertions is provided consisting of biological elements, relationships among the biological elements, and relationship descriptors characterizing the properties of the elements and relationships. A biological element may be selected from the database and a logical simulation may be performed within the biological database, from the selected biological element, through relationship descriptors, along a path defined by potentially causative biological elements to discern a biological element hypothetically responsible for the change in the selected biological element.

Abstract: A method of stratifying a set of disease-exhibiting patients prior to clinical trial of a target therapy begins by using a molecular footprint derived from a knowledgebase and other patient data to identify genes that are differentially expressed in a direction consistent with increase in the target activity. Therapeutic target “signaling strength” in individual patients of the set is then assessed using the genes identified and a strength algorithm. Based on their therapeutic target signaling strength, the set of disease-exhibiting patients are then stratified along a continuum. One or more gene expressions or other biomarkers may be specified for use in categorizing other disease-exhibiting patient populations. Alternative therapeutic targets are analyzed with respect to the likely non-responders, as evidenced by their differential signaling strength.

Abstract: One or more measurement signatures are derived from a knowledge base of casual biological facts, where a signature is a collection of measured node entities and their expected directions of change with respect to a reference node. The knowledge base may be a directed network of experimentally-observed casual relationships among biological entities and processes, and a reference node represents a perturbation. A degree of activation of a signature is then assessed by scoring one or more “differential” data sets against the signature to compute an amplitude score. The amplitude score quantifies fold-changes of measurements in the signature. In one particular embodiment, the amplitude score is a weighted average of adjusted log-fold changes of measured node entities in the signature, wherein an adjustment applied to the log-fold changes is based on their expected direction of change. In an alternative embodiment, the amplitude score is based on quantity effects.

Abstract: A “Specificity” statistic (or metric) is computed as a means to identify amplitude scores associated with a signature that can be attributed with high probability to a specific biological entity or process represented by the signature. Preferably, Specificity is computed by assessing a likelihood of a given null hypothesis, namely, that an amplitude score is not representative of the specific signature but, instead, is representative of a general trend in the applicable data set that can be measured by any signature that is comparable to the signature of interest. In a typical implementation, a first step to compute the Specificity metric is to construct a set of comparable signatures. Next, an amplitude score is computed for each of these signatures, preferably using the same data set. Then, the Specificity metric is computed, preferably as a two-tailed p-value, by placing the amplitude score for the signature of interest on a distribution of scores for the comparable signatures.

Abstract: One or more measurement signatures are derived from a knowledge base of casual biological facts, where a signature is a collection of measured node entities and their expected directions of change with respect to a reference node. The knowledge base may be a directed network of experimentally-observed casual relationships among biological entities and processes, and a reference node represents a perturbation. A degree of activation of a signature is then assessed by scoring one or more “differential” data sets against the signature to compute an amplitude score. The amplitude score quantifies fold-changes of measurements in the signature. In one particular embodiment, the amplitude score is a weighted average of adjusted log-fold changes of measured node entities in the signature, wherein an adjustment applied to the log-fold changes is based on their expected direction of change. In an alternative embodiment, the amplitude score is based on quantity effects.

Abstract: One or more measurement signatures are derived from a knowledge base of casual biological facts, where a signature is a collection of measured node entities and their expected directions of change with respect to a reference node. The knowledge base may be a directed network of experimentally-observed casual relationships among biological entities and processes, and a reference node represents a perturbation. A degree of activation of a signature is then assessed by scoring one or more “differential” data sets against the signature to compute an amplitude score. The amplitude score quantifies fold-changes of measurements in the signature. In one particular embodiment, the amplitude score is a weighted average of adjusted log-fold changes of measured node entities in the signature, wherein an adjustment applied to the log-fold changes is based on their expected direction of change. In an alternative embodiment, the amplitude score is based on quantity effects.

Abstract: The invention relates to computational methods, systems and apparatus useful in the analysis of sets of biomolecules in an accessible body fluid or tissue sample from a patient, which biomolecules collectively or individually are candidates to serve as biomarkers, i.e., biomolecules which together or individually upon detection or change are indicative that the patient is in some biological state, such as a diseased state. The methods permit one to examine such potential biological markers to determine whether each one is indeed present as a consequence of the biological state, or as an artifact of the biomarker search protocol.

Abstract: Method and system for managing and evaluating life science data. Life Science data is placed in a knowledge base, that may be used for a variety of analysis tasks. Creating a knowledge base from the life science data involves generating two or more nodes indicative of life science data, assigning to one or more pairs of nodes a representation descriptor that corresponds to a relationship between the nodes, and assembling the nodes and the relationship descriptor into a database, such that at least one of the nodes is joined to another node by a representation descriptor. In some embodiments, the representation descriptor includes a case frame that describes the relationships between elements of life science data.