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Patent
Philip Morris Products S.A. and Selventa | Date: 2014-08-12

Systems and methods are provided for curating and disseminating a network model. A representation of a network model is provided, and data is received that is representative of user actions. The user actions are directed to at least one element of the network model. A score is assigned to each respective element based on a number of user actions received for the respective element. A verified subset of edges is identified that have assigned scores that exceed a verification threshold, and a rejected subset of edges is identified that have assigned scores that are below a rejection threshold. The verified subset of edges and the associated nodes are provided as a curated network model, which omits the rejected subset of edges.


Patent
Selventa and Philip Morris Products S.A. | Date: 2015-06-26

A method to score a causally consistent network is provided by transforming the network into a hypothesis subnetwork, called a HYP (if the nodes have associated measurements) or a meta-HYP (if the nodes are themselves HYPs), and then applying known HYP scoring methods (e.g. (NPA, GPI, or the like) based on measurements or scores associated with nodes in the subnetwork. A method also is described for creating a HYP or meta-HYP with weights associated with each downstream node from a causally inconsistent network using a computational technique such as sampling of spanning trees. A further aspect is a method to transform a meta-HYP (with or without weights associated with each downstream node) into a HYP using the weights associated with each downstream node (where the weights are based on the scoring algorithms intended at the meta-HYP and HYP levels).


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. Scores that have Specificity p-values less than a particular value, e.g., 0.05, are considered to be scores that can be attributed with high confidence to the signature of interest.


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.


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. The logical simulation may be either a backward logical simulation, performed upstream through the relationship descriptors to discern a hypothetical responsible biological element, or a forward logical simulation, performed downstream through the relationship descriptors to discern the extent to which the perturbation generates the observed change in the selected biological element.


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.


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.


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.


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.


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.

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