A computerized method for generating a classifier for phenotypic prediction, comprising the steps of: (a) receiving, at a processing device, data indicative of a computational causal network model, wherein the data is representative of a biological system that contributes to a phenotype and includes: a plurality of nodes that represent biological entities in the biological system; and a plurality of edges connecting pairs of nodes among the plurality of nodes and representing relationships between the biological entities represented by the nodes; wherein one or more edges is associated with a direction value that represents a causal activation or causal suppression relationship between the biological entities represented by the nodes, and wherein each node is connected by an edge to at least one other node; (b) receiving, at the processing device, (i) a first set of data corresponding to activities of a first subset of biological entities obtained under a first set of conditions; and (ii) a second set of data corresponding to activities of the first subset of biological entities obtained under a second set of conditions different from the first set of conditions, wherein the first and second sets of conditions relate to the phenotype; (c) calculating, with the processing device, a set of activity measures for a first subset of nodes corresponding to the first subset of biological entities, each activity measure representing a difference between a first measurement under the first set of conditions from b(i) and a second measurement under the second set of conditions from b(ii); (d) generating, with the processing device, an inferred set of activity values for a second subset of nodes representing candidates of biological entities that contribute to the phenotype but whose activities are not measured, by identifying, for each particular node in the second subset of nodes, an activity value that optimizes a difference statement that represents the difference between the activity value of the particular node and the activity value or activity measure of nodes to which the particular node is connected by an edge within the computational causal network model, wherein the difference statement depends on the activity values of each node in the second subset of nodes, and wherein each activity value in the set of activity values is a linear combination of activity measures in the set of activity measures; and (e) generating, with the processing device using a machine learning technique, a classifier for the phenotype using both the set of activity measures and the set of activity values as inputs. (a) receiving, at a processing device, data indicative of a computational causal network model, wherein the data is representative of a biological system that contributes to a phenotype and includes: a plurality of nodes that represent biological entities in the biological system; and a plurality of edges connecting pairs of nodes among the plurality of nodes and representing relationships between the biological entities represented by the nodes; wherein one or more edges is associated with a direction value that represents a causal activation or causal suppression relationship between the biological entities represented by the nodes, and wherein each node is connected by an edge to at least one other node; a plurality of nodes that represent biological entities in the biological system; and a plurality of edges connecting pairs of nodes among the plurality of nodes and representing relationships between the biological entities represented by the nodes; wherein one or more edges is associated with a direction value that represents a causal activation or causal suppression relationship between the biological entities represented by the nodes, and wherein each node is connected by an edge to at least one other node; (b) receiving, at the processing device, (i) a first set of data corresponding to activities of a first subset of biological entities obtained under a first set of conditions; and (ii) a second set of data corresponding to activities of the first subset of biological entities obtained under a second set of conditions different from the first set of conditions, wherein the first and second sets of conditions relate to the phenotype; (c) calculating, with the processing device, a set of activity measures for a first subset of nodes corresponding to the first subset of biological entities, each activity measure representing a difference between a first measurement under the first set of conditions from b(i) and a second measurement under the second set of conditions from b(ii); (d) generating, with the processing device, an inferred set of activity values for a second subset of nodes representing candidates of biological entities that contribute to the phenotype but whose activities are not measured, by identifying, for each particular node in the second subset of nodes, an activity value that optimizes a difference statement that represents the difference between the activity value of the particular node and the activity value or activity measure of nodes to which the particular node is connected by an edge within the computational causal network model, wherein the difference statement depends on the activity values of each node in the second subset of nodes, and wherein each activity value in the set of activity values is a linear combination of activity measures in the set of activity measures; and (e) generating, with the processing device using a machine learning technique, a classifier for the phenotype using both the set of activity measures and the set of activity values as inputs.