System and method for identifying genetic disease and discovering disease associated genetic variants based on multiple instance learning

A method of identifying a genetic disease and discovering a disease-associated genetic variant, the method comprising:

• processing input data such that an input data processing unit uses instances which are genetic variant information of a patient and a bag of the instances as input data, and generating attention weights for the instances using an attention mechanism to process the input data;
• determining, using a multiple instance learning model, whether a disease of the patient is the genetic disease;
• based on determining that the disease of the patient is the genetic disease, discovering the disease-associated genetic variant that causes the disease of the patient using the attention weights for the instances;
• processing, as a bag label, whether the disease of the patient is a genetic disease caused by a genetic variant;
• generating first attention weights for the instances which are degrees to which the instances contribute to the identification of the genetic disease of the patient using the attention mechanism;
• processing the input data by reflecting the first attention weights for the instances;
• embedding the respective instances into low-dimensional vectors with a same dimension using respective neural networks;
• projecting the low-dimensional vectors onto one manifold using weight matrices and an activation function to obtain embedding vectors identical to each other;
• generating second attention weights for the embedding vectors using the attention mechanism;
• performing a pooling process of treating the embedding vectors as one vector;
• determining that the disease of the patient is the genetic disease caused by the genetic variant when a predicted value of the bag label is equal to or greater than a preset reference;
• discovering the disease-associated genetic variant that causes the disease of the patient using the first attention weights for the instances;
• generating an instance label for the instances using the first attention weights for the instances;
• and
• retraining the multiple instance learning model using the instance label.