Applications of complex adaptive systems metrology to identify quantitative edge targets for drug development using network graphs with time series nodes

A complex adaptive systems metrology (CASM) system for identifying quantitative edge targets for at least one of drug development and health care using network graphs with time-series nodes, the system comprising:

• at least one processing device;
• and
• memory storing instructions that when executed cause the at least one processing device to perform operations comprising:
• receiving multivariate time series data comprising at least two time-series action variables representing an individual complex adaptive system, wherein at least one time-series action variable operates as an independent action variable and at least one time-series action variable operates as a dependent action variable, and wherein each time-series action variable corresponds to a node in a network graph;
• decomposing the time series data to distinguish evidence for temporal interactions between nodes from linear and nonlinear trends in the action variables;
• digitizing each action variable having more than two levels to a set of digital time series comprised of zeros and ones to generate analysis parameters, wherein the analysis parameters include at least one independent action variable level for one or more independent action variables and at least one dependent action variable level for one or more dependent action variables;
• determining additional analysis parameters by generating a plurality of additional sets of digital time series, the generating including applying operationally defined rules to the digitized set of digital time series, wherein the additional analysis parameters include at least one of episode length, episode criterion, delay of effect, and persistence of effect;
• cross-classifying each digital time series for a respective independent action variable with each digital time series for a respective dependent action variable, the cross-classifying comprising generating one or more multidimensional arrays of tables;
• computing, for each of the tables, a raw and unstandardized temporal-interaction score;
• standardizing each raw and unstandardized temporal-interaction score so that each standardized temporal-interaction score represents one score from a distribution of potential scores defined by the time series data in combination with a CASM scoring protocol, wherein the distribution of potential scores has a mean of zero and a standard deviation of one unless zero is the only potential score;
• generating a summary temporal-interaction score for each multidimensional array, the summary temporal-interaction score representing a quantitative edge weight between a pair of time-series nodes in the network graph, wherein a positive summary temporal-interaction score quantifies evidence for an excitatory edge between the nodes and a negative summary temporal-interaction score quantifies evidence for an inhibitory edge between the nodes;
• determining, based on the generated summary temporal-interaction score, a quantitative significance estimate of the summary temporal-interaction score using permutation tests that randomly permute a temporal order of one or more of the time series;
• and
• identifying, based on the summary temporal-interaction score and the quantitative significance estimate, at least one quantitatively significant edge in the network graph as a drug development target or a health care target, wherein the identified edge target represents a temporal interaction between two or more action variables capable of being up-regulated or down-regulated by a drug, wherein the independent action variable is drug dose and at least one dependent action variable is at least one of a biological, psychological, or social action variable.

A non-transitory computer-readable medium for a complex adaptive systems metrology (CASM) for identifying quantitative edge targets for at least one of drug development or health care using network graphs with time-series nodes, comprising:

• at least one processor;
• and
• a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• receiving multivariate time series data comprising at least two time-series action variables representing an individual complex adaptive system, wherein at least one time-series action variable operates as an independent action variable and at least one time-series action variable operates as a dependent action variable, and wherein each time-series action variable corresponds to a node in a network graph;
• decomposing the time series data to distinguish evidence for temporal interactions between nodes from linear and nonlinear trends in the action variables;
• digitizing each action variable having more than two levels to a set of digital time series comprised of zeros and ones to generate analysis parameters, wherein the analysis parameters include at least one independent action variable level for one or more independent action variables and at least one dependent action variable level for one or more dependent action variables;
• determining additional analysis parameters by generating a plurality of additional sets of digital time series, the generating including applying operationally defined rules to the digitized set of digital time series, wherein the additional analysis parameters include at least one of episode length, episode criterion, delay of effect, and persistence of effect;
• cross-classifying each digital time series for a respective independent action variable with each digital time series for a respective dependent action variable, the cross-classifying comprising generating one or more multidimensional arrays of tables;
• computing, for each of the tables, a raw and unstandardized temporal-interaction score;
• standardizing each raw and unstandardized temporal-interaction score so that each standardized temporal-interaction score represents one score from a distribution of potential scores defined by the time series data in combination with a CASM scoring protocol, wherein the distribution of potential scores has a mean of zero and a standard deviation of one unless zero is the only potential score;
• generating a summary temporal-interaction score for each multidimensional array, the summary temporal-interaction score representing a quantitative edge weight between a pair of time-series nodes in the network graph, wherein a positive summary temporal-interaction score quantifies evidence for an excitatory edge between the nodes and a negative summary temporal-interaction score quantifies evidence for an inhibitory edge between the nodes;
• determining, based on the generated summary temporal-interaction score, a quantitative significance estimate of the summary temporal-interaction score using permutation tests that randomly permute a temporal order of one or more of the time series;
• and
• identifying, based on the summary temporal-interaction score and the quantitative significance estimate, at least one quantitatively significant edge in the network graph as a drug development target or a health care target, wherein the identified edge target represents a temporal interaction between two or more action variables capable of being up-regulated or down-regulated by a drug, wherein the independent action variable is drug dose and at least one dependent action variable is at least one of a biological, psychological, or social action variable.