Abstract: 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.

Abstract: 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.

Abstract: Systems and methods are provided for assessing perturbation of a target biological tissue caused by exposure to an agent. A set of scores indicative of a perturbation of the target biological tissue is determined based on a set of contrast data and a computational causal network model of xenobiotic metabolism in the target biological tissue and a surrogate biological tissue. The contrast data includes differences between activity measures of measurable nodes obtained from the surrogate biological tissue while exposed to the agent, and while exposed to a control. To determine the set of scores, a set of values is computed that indicates a perturbation of the surrogate biological tissue caused by the agent. A correlation is identified between the set of values for the backbone nodes obtained from the surrogate biological tissue and the set of scores that is indicative of a perturbation of the target biological tissue.

Abstract: Systems and methods for assessing a subject's sample to predict the subject's biological status, such as a smoker status. The computer-implemented method includes receiving, by a computer system including at least one hardware processor, a data set associated with the sample. The data set comprises quantitative expression data for a set of genes less than a whole genome, the set of genes comprising AHHR, CDKN1C, LRRN3, PID1, GPR15, SASH1, CLEC10A, LINC00599, P2RY6, DSC2, F2R, SEMA6B, and TLR5. The at least one hardware processor generates a score based on the quantitative expression data for the set of genes in the received data set, wherein the score is based on fewer than 40 genes and is indicative of a predicted smoking status of the subject.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods are provided for assessing perturbation of a target biological tissue caused by exposure to an agent. A set of scores indicative of a perturbation of the target biological tissue is determined based on a set of contrast data and a computational causal network model of xenobiotic metabolism in the target biological tissue and a surrogate biological tissue. The contrast data includes differences between activity measures of measurable nodes obtained from the surrogate biological tissue while exposed to the agent, and while exposed to a control. To determine the set of scores, a set of values is computed that indicates a perturbation of the surrogate biological tissue caused by the agent. A correlation is identified between the set of values for the backbone nodes obtained from the surrogate biological tissue and the set of scores that is indicative of a perturbation of the target biological tissue.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.