SYSTEMS AND METHODS FOR GENERATING A GENOTYPIC CAUSAL MODEL OF A DISEASE STATE
A system for generating a genotypic causal model of a disease state includes a computing device that generates a causal graph containing genotypic causal nodes and connected symptomatic causal nodes, which contains causal paths from gene combinations to symptomatic datums. Genotypic causal nodes and/or connected symptomatic causal nodes may be generated by feature learning algorithms from training data.
1 . A system for generating a genotypic causal model of a disease state, the system comprising a computing device configured to perform the steps of:
generating a machine-learning model including a causal graph, wherein generating the machine learning model further comprises:
generating, using a first feature learning algorithm, a plurality of genotypic causal nodes, wherein each genotypic causal node includes a disease state and a gene combination correlated with the disease state;
receiving a genetic sequence comprising a series of genes identified in a nucleotide sequence of chromosomal nucleic acid of a human subject as input;
outputting at least a path in the causal graph from inputs in the genetic sequence to a determined disease state, wherein the at least a path contains at least a genotypic node; and
generating a causal model, as a function of the at least a path in the causal graph including the at least a genotypic node, wherein the causal model comprises a data structure describing disease states and causal gene data.
2 . The system of claim 1 , wherein the computing device is further configured to perform the step of determining, as a function of the causal gene data, one or more lifestyle factors.
3 . The system of claim 2 , wherein determining the one or more lifestyle factors comprises determining the one or more lifestyle factors using a lifestyle factor machine-learning model.
4 . The system of claim 3 , wherein determining the one or more lifestyle factors comprises using the lifestyle factor machine learning model comprises:
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of lifestyle factors; and
training the lifestyle factor machine learning model using the lifestyle factor training data.
5 . The system of claim 3 , wherein determining the one or more lifestyle factors comprises using the lifestyle factor machine learning model comprises:
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of positive lifestyle factors;
training the lifestyle factor machine learning model using the lifestyle factor training data; and
generating one or more positive lifestyle factor as a function of the lifestyle factor machine learning model.
6 . The system of claim 3 , wherein determining the one or more lifestyle factors comprises using the lifestyle factor machine learning model comprises:
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of negative lifestyle factors;
training the lifestyle factor machine learning model using the lifestyle factor training data; and
generating one or more negative lifestyle factor as a function of the lifestyle factor machine learning model.
7 . The system of claim 1 , wherein generating the causal model comprises generating a report describing the disease states and causal gene data.
8 . The system of claim 7 , wherein generating the report describing the disease states and causal gene data comprises generating the report describing the disease states and causal gene data using a large language model.
9 . The system of claim 1 , wherein the computing device is further configured to perform the step of displaying the causal model to the user.
10 . The system of claim 1 , wherein receiving a genetic sequence comprises receiving the genetic sequence from a user database.
11 . A method for generating a genotypic causal model of a disease state, the method comprising:
generating, using a computing device, a machine-learning model including a causal graph, wherein generating the machine learning model further comprises:
generating, using a first feature learning algorithm, a plurality of genotypic causal nodes, wherein each genotypic causal node includes a disease state and a gene combination correlated with the disease state;
receiving, using the computing device, a genetic sequence comprising a series of genes identified in a nucleotide sequence of chromosomal nucleic acid of a human subject as input;
outputting, using the computing device, at least a path in the causal graph from inputs in the genetic sequence to a determined disease state, wherein the at least a path contains at least a genotypic node; and
generating, using the computing device, a causal model, as a function of the at least a path in the causal graph including the at least a genotypic node, wherein the causal model comprises a data structure describing disease states and causal gene data.
12 . The method of claim 11 , further comprising determining, by the computing device, as a function of the causal gene data, one or more lifestyle factors.
13 . The method of claim 12 , wherein determining the one or more lifestyle factors comprises determining the one or more lifestyle factors using a lifestyle factor machine-learning model.
14 . The method of claim 13 , wherein determining the one or more lifestyle factors comprises using the lifestyle factor machine learning model comprises:
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of lifestyle factors; and
training the lifestyle factor machine learning model using the lifestyle factor training data.
15 . The method of claim 13 , wherein determining the one or more lifestyle factors comprises using the lifestyle factor machine learning model comprises:
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of positive lifestyle factors;
training the lifestyle factor machine learning model using the lifestyle factor training data; and
generating one or more positive lifestyle factor as a function of the lifestyle factor machine learning model.
16 . The method of claim 13 , wherein determining the one or more lifestyle factors comprises using the lifestyle factor machine learning model comprises:
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of negative lifestyle factors;
training the lifestyle factor machine learning model using the lifestyle factor training data; and
generating one or more negative lifestyle factor as a function of the lifestyle factor machine learning model.
17 . The method of claim 11 , wherein generating the causal model comprises generating a report describing the disease states and causal gene data.
18 . The method of claim 17 , wherein generating the report describing the disease states and causal gene data comprises generating the report describing the disease states and causal gene data using a large language model.
19 . The method of claim 11 , further comprising displaying, by the computing device, the causal model to the user.
20 . The method of claim 11 , further comprising receiving, by the computing device, a genetic sequence comprises receiving the genetic sequence from a user database.