| CPC G16B 5/00 (2019.02) [C12Q 1/025 (2013.01); G01N 33/5008 (2013.01); G01N 33/68 (2013.01)] | 43 Claims |
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1. A method for identifying a modulator of angiogenesis, said method comprising:
(1) obtaining a first data set from a model for angiogenesis that uses cells associated with angiogenesis to represent a characteristic aspect of angiogenesis, wherein the first data set represents one or more of genomic data, lipidomic data, proteomic data, metabolomic data, transcriptomic data, and single nucleotide polymorphism (SNP) data characterizing the cells associated with angiogenesis;
(2) obtaining a second data set from the model for angiogenesis, wherein the second data set represents one or more functional activities or cellular responses of the cells associated with angiogenesis;
(3) generating a first causal relationship network model among the one or more of genomic data, lipidomic data, proteomic data, metabolic data, transcriptomic data, and single nucleotide polymorphism (SNP) data characterizing the cells associated with angiogenesis, and the one or more functional activities or cellular responses of the cells associated with angiogenesis based on the first data set and the second data set using a programmed computing system including a plurality of processors, wherein generating the first causal relationship network comprises:
(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the one or more of genomic data, lipidomic data, proteomic data, metabolomic data, transcriptomic data, and single nucleotide polymorphism (SNP) data and the one or more functional activities or cellular responses of the cells associated with angiogenesis;
(ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and
(iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors;
wherein relationships in the first causal relationship network model and causality in the first causal relationship network model are determined based on the first data set and the second data set and not based on previously identified or known biological relationships between variables;
(4) generating a differential causal relationship network from the first causal relationship network model and a second causal relationship network model based on control cell data using a computing device by steps including:
(i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and
(ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model; and
(5) identifying, from the differential causal relationship network, a causal relationship unique in angiogenesis, wherein a gene, lipid, protein, metabolite, transcript, or SNP associated with the unique causal relationship is identified as a modulator of angiogenesis.
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