Method and system for predicting therapeutic agent resistance and for defining the genetic basis of drug resistance using neural networks

A method for predicting resistance of a pathogen to a therapeutic agent comprising:

• (a) providing a trained neural network;
• (b) providing a determined genetic sequence from the pathogen;
• and
• (c) predicting resistance of the pathogen to the therapeutic agent using the determined genetic sequence and the trained neural network.

A method for predicting resistance of a pathogen to a therapeutic agent comprising:

• (a) providing a neural network;
• (b) training the neural network on a training data set, wherein each member of the training data set corresponds to a genetic mutation that correlates to a change in therapeutic agent resistance;
• (c) providing a determined genetic sequence from the pathogen;
• and
• (d) predicting resistance of the pathogen to the therapeutic agent using the determined genetic sequence and the trained neural network.

A method of designing a therapeutic agent treatment regimen for a patient afflicted with a disease comprising:

• (a) providing a determined genetic sequence from the disease;
• (b) inputting the determined genetic sequence into a trained neural network;
• (c) predicting resistance of the disease to a therapeutic agent using the determined genetic sequence and the trained neural network;
• and
• (d) using the predicted drug resistance to design the therapeutic drug treatment regimen to treat the patient afflicted with the disease.

A method of predicting the probability of a patient developing a genetic disease comprising:

• (a) providing a trained neural network;
• (b) providing a determined genetic sequence from a patient sample;
• and
• (c) determining the probability of the patient of developing the genetic disease using the determined genetic sequence and the trained neural network.

A method for identifying a new mutation that confers resistance to a therapeutic agent comprising:

• (a) providing a first trained neural network, wherein the number of input nodes for said first trained neural network is equal to the number of mutations known to confer therapeutic resistance to a therapeutic agent;
• (b) providing a second trained neural network, wherein the number of input nodes of said second trained neural network comprises the number of mutations known to confer therapeutic resistance to a therapeutic agent plus at least one additional mutation;
• (c) providing a test data set;
• (d) inputting the test data set into the first and second trained neural networks;
• (e) comparing the output of the first and second trained neural networks to determine whether the additional mutation confers therapeutic drug resistance to a disease.