Enhancing Genomic Data Exploration with AI-Driven Natural Language Queries | AWS for Industries

Learn About Amazon VGT2 Learning Manager Chanci Turner

In the fast-paced landscape of Life Sciences, researchers and healthcare practitioners encounter the challenge of effectively accessing and analyzing extensive amounts of intricate genomic, clinical, and imaging data. Traditional querying methods often necessitate specialized technical expertise in SQL and database architectures, resulting in research workflow bottlenecks and hindering the accessibility of critical insights.

With the recent introduction of Amazon Bedrock Knowledge Bases, users can now leverage natural language querying to extract structured data from sources like Amazon Redshift. Researchers can pose questions in simple terms, such as, “What is the leading gene mutation identified in all patients?” or “Provide me with all information regarding the OR6Y1 gene.” These inquiries will yield precise data from genomic databases, patient records, and medical imaging archives. Amazon Bedrock Knowledge Bases seamlessly translates these natural language questions into optimized SQL queries.

This innovative method accelerates research workflows, promoting quicker discoveries and more efficient clinical decision-making. We will delve into how Amazon Bedrock Knowledge Bases can be utilized to revolutionize the interaction of Life Sciences organizations with their valuable data stored in Amazon Redshift.

Solution Overview

To demonstrate this capability, we will construct a solution using sample patient genomics data and configure Amazon Redshift as the knowledge base. This will allow users and applications to access this information through natural language prompts. The following (Figure 1) outlines the solution.

Figure 1 – Solution Architecture for Genomic Data Analysis Using Natural Language

The steps to develop and operate the solution are as follows:

1. Load Patients’ Data: Import the sample patient genomics data into Amazon Redshift using the copy process.
2. Setup Knowledge Base: Configure Amazon Redshift as a knowledge base within Amazon Bedrock, granting access and syncing the metadata.
3. Prompt in Natural Language: Users or applications initiate prompts in natural language (illustrated using a testing interface).
4. Generate and Run the Query: Amazon Bedrock constructs the query based on the prompt and Amazon Redshift metadata, executing it on the Amazon Redshift instance.
5. Return Results: The results from the query are retrieved from Amazon Redshift.
6. Return Response in Natural Language: Amazon Bedrock interprets the tabular results and translates them into a natural language response.

Implementation

The following tutorial will guide you through the process of uploading sample patient data from data files stored in an Amazon Simple Storage Service (Amazon S3) bucket into your Amazon Redshift database tables, and then configuring Amazon Bedrock Knowledge Bases for natural language interactions with the data.

Step 1: Download the Data Files

Download a collection of sample data files to your computer and then upload them to an S3 bucket.

1. Download the zipped file: samplepatientdata.zip. The clinical datasets were generated using Synthea, while the OMICS and Images data were sourced from The Cancer Genome Atlas (TGCA) open data sets.
2. Extract the files to a folder on your computer.

Step 2: Upload the Files to an S3 Bucket

Create an S3 bucket and upload the data files.

1. Create a bucket in Amazon S3. For detailed instructions on creating a bucket, see Creating a bucket.
2. Upload the data files to the new S3 bucket. In the Upload wizard, select Add files, and follow the Amazon S3 console instructions to upload all the files you downloaded and extracted.

Step 3: Create Redshift Serverless Instance

Set up an Amazon Redshift Serverless instance, create tables, and load the data from the S3 bucket.

1. Follow the documentation for Creating a data warehouse with Amazon Redshift Serverless to create the data warehouse instance.
2. Download the SQL file: SQL.txt to your computer. Replace “S3://redshift-kb-bedrock-logdata” with the name of your S3 bucket.
3. Open Redshift Query Editor V2 by clicking on Query Data and connect to your Amazon Redshift Serverless Instance using the current admin credentials.
4. Execute all SQL commands found in the SQL.txt file. This step will create tables and load the data into them from your S3 bucket. Ensure that these tables are populated: patient_reference_data_rs, patients_rs, gene_mutation_rs, gene_copy_number_rs, image_data_rs.

Step 4: Setup Bedrock Knowledge Bases

Establish Amazon Bedrock Knowledge Bases for the Amazon Redshift database and sync the data.

1. Prerequisites: If you are utilizing an AWS Identity and Access Management (IAM) role, ensure it has the appropriate policy permissions attached to it before executing operations on Amazon Bedrock Knowledge Bases. For guidance, see Prerequisites for creating an Amazon Bedrock knowledge base with a structured data store.
2. Create your Knowledge Bases, incorporating a structured data store while setting it up by selecting the option.
3. After naming and describing the Knowledge Base, choose Amazon Redshift as the query engine and establish a new IAM service role for resource management.
4. In the connection settings, select Redshift Serverless, authenticate using the IAM role created earlier, and choose a metadata database from your Amazon Redshift options (we selected ‘dev’ for this tutorial).
5. Grant the IAM role specific permissions to retrieve data from the selected tables by executing the GRANT command for your Amazon Redshift database.
6. Sync your Amazon Redshift database with your Knowledge Base. Select your Knowledge Base, choose the Amazon Redshift database source, and click Sync. Once the sync is complete, the Status will indicate COMPLETE. Remember, whenever you alter your database schema, you must sync the changes.

Step 5: Test the Amazon Bedrock Knowledge Bases for Amazon Redshift Database

Run queries against the newly created Amazon Bedrock Knowledge Bases for Amazon Redshift database. You can set up your application to engage with your data in a more streamlined way. For further insights into communication strategies, consider visiting SHRM’s resources on communication, which are highly regarded in the field.

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