Patent ID: 8024277
Filing Date: 2011-09-20
Classification: G16B

Abstract:
1. A method for establishment of causal regulation network from time series data, comprising the following steps: obtaining a time-series dataset comprising data obtained at a series of time points; generating from a computer tool a first predetermined amount of causal regulation network templates as candidate networks, each candidate network structure being represented by an integer array for genetic algorithm operations; fitting data of said dataset into said candidate networks by downhill simplex method; calculating joint probability density (score) of said fitted candidate networks, S, according to the following equation, respectively: wherein score(S) approximates the logarithm of the joint probability density of a candidate network, S, and the parameters, {circumflex over (Θ)}, represent the values of the parameters Θ that maximize the likelihood function P(x|S,Θ), wherein: wherein p(x wherein exponent, ω wherein ε is a penalty against structure complexity, d equals to the number of parameters in said candidate network, n represents number of data included in said fitted candidate networks, T represents number of time points 0, 1, 2, . . . , T−1, and R represents number of repeats of said measurement; selecting from said fitted candidate networks a second predetermined number of candidate networks with the highest score values; mating and mutating said selected candidate networks according to the genetic algorithm to generate offspring candidate networks; fitting data of said dataset into said offspring networks and calculating score values of said offspring networks; determining whether the average of the obtained score values reaches a plateau; if not, repeating steps of said mating and mutating and said score calculation; otherwise, selecting a third predetermined number of the offspring candidate networks with highest score values; and outputting said selected offspring candidate networks as result of establishment of causal regulation network.