System and method for polygenic phenotypic trait predisposition assessment using a combination of dynamic network analysis and machine learning

An apparatus for Phenotypic Trait Predisposition Assessment Based on the Multiple Genetic Variations in DNA Using a Combination of Dynamic Network Analysis and Machine Learning, the apparatus comprising:

• a processor;
• and
• a memory having executable instructions stored thereon that when executed by the processor cause the processor to:
• receive a genetic data and a non-genetic data of an individual;
• calculate a phenotypic trait predisposition score for the individual, wherein the first phenotypic trait predisposition score is a reference score;
• organize a knowledge base repository using dynamic network analysis of a plurality of genetic variants and of a plurality of phenotypic traits into a heterogeneous knowledge network model;
• assess regulatory, catalytic or inhibitory utility of genetic factors by determining existence of said genetic factors within biological pathways, the biological pathways comprising at least one of metabolic pathways, regulatory networks and signal transduction pathways;
• calibrate the phenotypic trait predisposition score for the individual using a machine learning analysis that relates the plurality of genetic variations to the plurality of phenotypic traits;
• and
• calibrate the heterogeneous knowledge network model using the genetic data, the non-genetic data, the first phenotypic trait predisposition score and the second phenotypic trait predisposition score.
• represent, through an advanced programming interface (API), the results of the predisposition assessment to client applications.

A non-transitory computer readable media comprising program code that when executed by a programmable processor causes execution of a method for Phenotypic Trait Predisposition Assessment Based on the Multiple Genetic Variations in DNA Using a Combination of Dynamic Network Analysis and Machine Learning, the computer readable media comprising:

• receiving a genetic data and a non-genetic data of an individual;
• calculating a phenotypic trait predisposition score for the individual, wherein the first phenotypic trait predisposition score is a reference score;
• organizing a knowledge base repository using dynamic network analysis of a plurality of genetic variants and of a plurality of phenotypic traits into a heterogeneous knowledge network model;
• assessing regulatory, catalytic or inhibitory role utility of genetic factors by determining existence of said genetic factors within biological pathways, the biological pathways comprising at least one of metabolic pathways, regulatory networks and signal transduction pathways;
• calibrating the phenotypic trait predisposition score for the individual using a machine learning analysis that relates the plurality of genetic variations to the plurality of phenotypic traits;
• and
• calibrating the heterogeneous knowledge network model using the genetic data and the non-genetic data.