The variant artificial intelligence easy scoring (VARIES) system.
Taghrid Aloraini1, Abdulrhman Aljouie2, Rashed Alniwaider1
1Division of Translational Pathology, Department of Laboratory Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia; King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Computers in Biology and Medicine
|May 19, 2022
Summary
This study demonstrates how to build a powerful genomic AI prediction tool using Google Cloud Platform
Area of Science:
- Genomics
- Bioinformatics
- Artificial Intelligence in Healthcare
Background:
- Medical artificial intelligence (MAI) applies AI to healthcare, including genetic variant classification.
- Developing AI tools often requires significant coding expertise.
Purpose of the Study:
- To share the experience of variant classification using the Variant Artificial Intelligence Easy Scoring (VARIES) platform and Google Cloud AutoML.
- To provide a guideline for creating genomic AI prediction tools with high predictive power without prior coding experience.
Main Methods:
- Utilized exome sequencing data from 1410 individuals (80% training, 20% testing).
- Employed the Google Cloud Platform and the TRIPOD checklist for model development and validation.
- Leveraged the Variant Artificial Intelligence Easy Scoring (VARIES) platform for variant classification.
Main Results:
- The VARIES model achieved optimal training results rapidly, with a loss value near zero in approximately 4 minutes.
- Performance on the testing dataset included an F1 micro-average of 0.64 and an AUC (one-over-rest) micro-average of 0.81.
- The model demonstrated high predictive ability for variant classification.
Conclusions:
- A systematic guideline for creating a high-predictive genomic AI tool was presented.
- The process utilized a user-friendly graphical interface on Google Cloud Platform.
- This approach enables AI tool development with minimal prior software programming experience.
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