Patent application number | Description | Published |
20140114987 | SYSTEMS AND METHODS FOR QUANTIFYING THE IMPACT OF BIOLOGICAL PERTURBATIONS - Systems and methods are described for quantifying the response of a biological system to one or more perturbations. First and second datasets corresponding to a response of a biological system to first and second treatments are received. A plurality of computational network models that represent the biological system are provided, each model including nodes representing a plurality of biological entities and edges representing relationships between the nodes in the model. A first set of scores is generated, representing the perturbation of the biological system based on the first dataset and the plurality of models, and a second set of scores representing the perturbation of the biological system based on the second dataset and the plurality of computational models. One or more biological impact factors are generated based on each of the first set and second set of scores that represent the biological impact of the perturbation on the biological system. | 04-24-2014 |
20140172398 | SYSTEMS AND METHODS FOR NETWORK-BASED BIOLOGICAL ASSESSMENT - Systems and methods are directed to computerized methods and one or more computer processors for quantifying the perturbation of a biological system in response to an agent. A set of treatment data corresponding to a response of a biological system to an agent and a set of control data are received. A computational causal network model represents the biological system and includes nodes representing biological entities, edges representing relationships between the biological entities, and direction values representing the expected direction of change between the control data and the treatment data. Activity measures are calculated and represent a difference between the treatment data and the control data, and weight values are calculated for the nodes. A score for the computational model is generated representative of the perturbation of the biological system to the agent and is based on the direction values, the weight values and the activity measures. | 06-19-2014 |
20140207385 | SYSTEMS AND METHODS FOR CHARACTERIZING TOPOLOGICAL NETWORK PERTURBATIONS - Systems, computerized methods and products are disclosed herein for determining metrics for nodes in a network model of a biological system. Such systems and computerized methods can be used to quantify the response of a biological system to one or more perturbations based on measured activity data of a subset of entities in the biological system. Based on the activity data and a network model of the biological system, centrality values representative of the relative importance of a node in the network are derived. The centrality values are used for characterizing topological perturbations in the network, such as for performing sensitivity analysis, visualizing topological effects of a perturbation in the biological system, or deriving a score quantifying the response of the biological system to a perturbation such as exposure to a chemical agent. | 07-24-2014 |
20140214336 | SYSTEMS AND METHODS FOR NETWORK-BASED BIOLOGICAL ACTIVITY ASSESSMENT - Systems and methods are disclosed herein for quantifying the response of a biological system to one or more perturbations based on measured activity data from a subset of the entities in the biological system. Based on the activity data and a network model of the biological system that describes the relationships between measured and non-measured entities, activities of entities that are not measured are inferred. The inferred activities are used for deriving a score quantifying the response of the biological system to a perturbation such as a response to a treatment condition. The score may be representative of the magnitude and topological distribution of the response of the network to the perturbation. | 07-31-2014 |
20140352706 | ISOPROPYLMALATE SYNTHASE FROM NICOTIANA TABACUM AND METHODS AND USES THEREOF - The present invention relates to a mutant, non-naturally occurring or transgenic plant cell comprising: (i) at least one polynucleotide comprising, consisting or consisting essentially of a sequence encoding an isopropylmalate synthase and having at least 60% sequence identity to SEQ ID NO:1 or SEQ ID NO:10 or SEQ ID NO: 12 or SEQ ID NO:14; or (ii) a polypeptide encoded by said polynucleotide(s); or (iii) a polypeptide having at least 60% sequence identity to SEQ ID NO:2 or SEQ ID NO:11 or SEQ ID NO:13 or SEQ ID NO:15; or (iv) a construct, vector or expression vector comprising said polynucleotide sequence(s), optionally wherein said construct, vector or expression vector additionally comprises a promoter comprising, consisting or consisting essentially of the sequence set forth in SEQ ID NO:8 or a variant thereof with at least about 60% identity thereto or a trichome promoter. | 12-04-2014 |
20150154353 | SYSTEMS AND METHODS FOR GENERATING BIOMARKER SIGNATURES WITH INTEGRATED DUAL ENSEMBLE AND GENERALIZED SIMULATED ANNEALING TECHNIQUES - Described herein are systems and methods for classifying a data set using an ensemble classification technique. Classifiers are iteratively generated by applying machine learning techniques to a training data set, and training class sets are generated by classifying the elements in the training data set according to the classifiers. Objective values are computed based on the training class sets, and objective values associated with different classifiers are compared until a desired number of iterations is reached, and a final training class set is output. | 06-04-2015 |
20150178639 | SYSTEMS AND METHODS FOR GENERATING BIOMARKER SIGNATURES WITH INTEGRATED BIAS CORRECTION AND CLASS PREDICTION - Described herein are systems and methods for correcting a data set and classifying the data set in an integrated manner. A training data set, a training class set, and a test data set are received. A first classifier is generated for the training data set by applying a machine learning technique to the training data set and the training class set, and a first test class set is generated by classifying the elements in the test data set according to the first classifier. For each of multiple iterations, the training data set is transformed, the test data set is transformed, and a second classifier is generated by applying a machine learning technique to the transformed training data set. A second test class set is generated according to the second classifier, and the first test class set is compared to the second test class set. | 06-25-2015 |
20150220838 | SYSTEMS AND METHODS RELATING TO NETWORK-BASED BIOMARKER SIGNATURES - Systems and methods are provided herein for generating a classifier for phenotypic prediction. A computational causal network model representing a biological system includes a plurality of nodes and a plurality of edges connecting pairs of nodes. A first set of data corresponding to activities of a first subset of biological entities obtained under a first set of conditions is received, and a second set of data corresponding to activities of the first subset of biological entities obtained under a second set of conditions is received. A set of activity measures representing a difference between the first and second sets of data for a first subset of nodes is calculated. A set of activity values for a second subset of nodes, which are unmeasured, is generated. A classifier is generated for the phenotypes based on the set of activity measures, the set of activity values, or both. | 08-06-2015 |