# US 2026/0301960 A1 · System and method for identifying genetic disease and discovering disease associated genetic variants based on multiple instance learning

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2026/0301960 A1
- Document ID: US20260301960A1
- Status: Published application
- Kind: A1
- Application number: 19/700,567
- Filed: 2026-06-08
- Published: 2026-10-01
- Earliest claimed: 2023-04-14
- Assignees: 3Billion
- Applicants: 3Billion
- Inventors: Kyongyeul LEE; Ho Heon KIM; Joo Yeup BAEK
- Web page: https://patentgrep.com/patent/US20260301960A1

## CPC

- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY

## Abstract

The present disclosure provides a system configured to identify a genetic disease and discover a disease-associated genetic variant, the system including a multiple instance learning model unit configured to derive identification of a genetic disease of a patient and discovery of a disease-associated genetic variant together using a multiple instance learning model configured to learn instances which are genetic variant information of the patient and a bag of the instances as input data and process, as a bag label, whether a disease of the patient is a genetic disease caused by a genetic variant.

## Claims

### Claim 1

A system configured to identify a genetic disease and discover a disease-associated genetic variant, the system comprising:
one or more processors comprising processing circuitry; and
memory storing instructions,
wherein the instructions, when executed by the one or more processors, cause the system to:
derive identification of the genetic disease of a patient and discovery of the disease-associated genetic variant together using a multiple instance learning model configured to learn instances which are genetic variant information of the patient and a bag of the instances as input data;
process, as a bag label, whether a disease of the patient is the genetic disease caused by a genetic variant;
generate 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 an attention mechanism;
process the input data by reflecting the first attention weights for the instances;
embed the respective instances into low-dimensional vectors with a same dimension using respective neural networks;
project the low-dimensional vectors onto one manifold using weight matrices and an activation function to obtain embedding vectors identical to each other;
generate second attention weights for the embedding vectors using the attention mechanism;
perform a pooling process of treating the embedding vectors as one vector;
determine 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;
discover the disease-associated genetic variant that causes the disease of the patient using the first attention weights for the instances;
generate an instance label for the instances using the first attention weights for the instances; and
retrain the multiple instance learning model using the instance label.

### Claim 2 (depends on 1)

The system of claim 1, wherein the multiple instance learning model is a multi-input model using the input data with various vector magnitudes.

### Claim 3 (depends on 1)

The system of claim 1, wherein the one or more processors comprises one or more data processing units.

### Claim 4 (depends on 1)

The system of claim 1, wherein the instances comprise feature vectors extracted from genetic variants, wherein:
for single nucleotide variants, the feature vectors comprise a pathogenicity score, allele frequency values from a population database, a conservation score, protein domain annotations, and a gene-level constraint metric; and
for structural variants, the feature vectors comprise a variant type indicator, variant length, breakpoint coordinates, and gene overlap counts.

### Claim 5 (depends on 1)

The system of claim 1, wherein the respective neural networks comprise:
a first encoder neural network for single nucleotide variants comprising at least three fully connected layers with ReLU activation functions;
a second encoder neural network for structural variants comprising at least four fully connected layers with LeakyReLU activation functions; and
wherein each encoder neural network projects to a common embedding dimension.

### Claim 6 (depends on 1)

The system of claim 1, wherein the attention mechanism comprises a gated attention mechanism applied after different variant types are integrated into a common embedding vector, and wherein variant-type-specific knowledge is not incorporated into gating factors.

### Claim 7 (depends on 1)

The system of claim 1, wherein the preset reference comprises a calibrated probability threshold determined through isotonic regression on a validation dataset to achieve a target sensitivity of at least 95%, and wherein the system further outputs a confidence interval for the determination estimated using Monte Carlo dropout.

### Claim 8 (depends on 1)

The system of claim 1, wherein retraining the multiple instance learning model comprises:
generating instance labels for instances with attention weights exceeding a dynamic threshold;
retraining the model using a combined loss function comprising a bag-level binary cross-entropy loss and a weighted instance-level binary cross-entropy loss;
decreasing the dynamic threshold in each iteration; and
terminating when instance label assignments stabilize or a maximum number of iterations is reached.

### Claim 9 (depends on 1)

The system of claim 1, wherein discovering the disease-associated genetic variant comprises:
computing a contribution score for each instance as a product of a corresponding first attention weight of the first attention weights, a cosine similarity between an embedding vector of the instance and a learned disease-positive prototype vector, and a variant-type-specific scaling factor; and
ranking the instances by contribution score and reporting top-ranked instances as candidate disease-associated variants.

### Claim 10 (depends on 1)

The system of claim 1, further comprising:
a clinical decision support interface configured to:
receive the identification of the genetic disease and the discovered disease-associated genetic variant;
retrieve treatment protocol data from a treatment database based on the identified genetic disease and the discovered disease-associated genetic variant;
generate a treatment recommendation comprising at least one of a pharmaceutical intervention, a gene therapy, or a surveillance protocol specific to the identified genetic disease; and
transmit the treatment recommendation to an electronic health record system.

### Claim 11

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.

### Claim 12 (depends on 11)

The method of claim 11, further comprising:
based on discovering that the disease-associated genetic variant is a single nucleotide variation in Guanidinoacetate methyltransferase (GAMT), selecting and administering a treatment to the patient, wherein the treatment comprises at least one of creatine supplementation or ornithine supplementation.

### Claim 13 (depends on 11)

The method of claim 11, further comprising:
based on discovering that the disease-associated genetic variant is a single nucleotide variation in Tuberous Sclerosis Complex 2 (TSC2), selecting and administering a treatment to the patient, wherein the treatment is an antiepileptic drug or a mechanistic Target of Rapamycin inhibitor.

### Claim 14 (depends on 11)

The method of claim 11, further comprising:
administering a treatment to the patient having the genetic disease caused by the genetic variant, wherein the genetic disease is associated with a genetic mutation that involves an insertion or deletion of one or more nucleotides, wherein the genetic disease is associated with ATP-Binding Cassette Transporter Sub-Family D Member 1 (ABCD1), and wherein the treatment is a Hematopoietic stem cell transplantation (HSCT).

### Claim 15 (depends on 11)

The method of claim 11, further comprising:
based on discovering that the disease-associated genetic variant is a pathogenic variant in ATP-Binding Cassette Transporter Sub-Family D Member 1 (ABCD1) causing X-linked adrenoleukodystrophy, selecting and administering a treatment to the patient, wherein the treatment is Hematopoietic stem cell transplantation (HSCT).