ML Genomic Pipeline Predictor for Compute Capacity
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Solution Overview
Problem
Genomic sequencing pipelines often fail due to infrastructure resource constraints, leading to wasted time and effort as scientists need to manually prepare and run stress tests to verify compute resource capacity, which can take hours or days and may still result in failed tests due to insufficient resources.
Innovation Solution
A machine learning-based system that predicts whether genomic sequencing test pipelines can be successfully completed in a given compute environment by training on datasets and providing immediate feedback on infrastructure capacity, allowing for prompt resource management and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual stress testing is performed to verify compute resource capacity, then infrastructure capacity can be verified, but time consumption increases significantly (hours or days)
Solution Approach 1:
The system performs preliminary action by training a machine learning model on historical stress test data before actual genomic pipeline execution. This pre-trained model can then immediately predict resource capacity requirements without requiring new manual stress tests, thus resolving the time-consuming nature of traditional verification while maintaining reliability
Solution Approach 2:
The system creates a virtual copy of the stress testing process through machine learning modeling. Instead of physically executing time-consuming stress tests, the ML model replicates the prediction capability by learning from historical test results, providing rapid capacity verification that mirrors traditional methods without the time penalty
2Reliability
If manual stress testing is performed to verify compute resource capacity, then infrastructure capacity can be verified, but effort and complexity increase
Solution Approach 1:
The system implements self-service by enabling the ML model to automatically predict resource capacity requirements without human intervention in the testing process. The model independently analyzes pipeline specifications and computes resource needs, eliminating the manual preparation and execution complexity while maintaining verification reliability
Solution Approach 2:
The system replaces the mechanical manual stress testing process with an intelligent ML-based prediction system. Instead of physically configuring and running stress tests, the ML model substitutes this mechanical process with computational prediction, reducing operational complexity while preserving capacity verification accuracy
3Productivity
If genomic sequencing pipelines are run without proper resource verification, then time and effort are saved, but pipeline failure rate increases
Solution Approach 1:
The system performs preliminary resource capacity prediction using the trained ML model before genomic pipeline execution. This advance prediction ensures that pipelines are only launched when resources are confirmed sufficient, preventing failures while maintaining rapid execution speed by eliminating the need for time-consuming manual verification
Data Source
AI summary
Embodiments predict genomic testing using machine learning. Embodiments receive one or more training datasets of a genomic pipeline comprising a plurality of training variables for each of a plurality of genomic tests and corresponding results of each of the genomic tests. Embodiments train a machine learning model using the training datasets and receive a new genomic workflow pipeline comprising new genomic testing variables. Embodiments then predict, using the trained machine learning model and new genomic testing variables, whether the new genomic workflow pipeline will be successfully completed within a first compute environment.