Selventa, Inc.

Genstruct, Inc.

Dexter Roydon Pratt

Ty Matthew Thomson

William M Ladd

Justin Sun

Keith O Elliston

Eric K Neumann

David Drubin

Dundee Navin Chandra

D Navin Chandra

Suresh Toby Segaran

Fatima Chandra, Legal Representative

David Kightley

Ted M Slater

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.

Determining whether a measurement signature is specific to a biological process

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.

Determining a confidence of a measurement signature score

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.

Method for quantifying amplitude of a response of a biological network

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.

System, method and apparatus for causal implication analysis in biological networks

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.

System, method and apparatus for assembling and mining life science data

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Reading, MA, United States of America

Dexter Roydon Pratt