ML Confidence Ranking for Computational Workflow Outputs
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Solution Overview
Problem
Converting biological data between formats and/or file types in computational workflows results in numerous and varying outputs, making manual evaluation inefficient for users, necessitating a solution to determine confidence values for individual outputs.
Innovation Solution
A machine learning model is trained to determine confidence values for potential workflow outputs using input/output pairs, utilizing techniques like supervised, semi-supervised, unsupervised, and reinforcement learning to analyze sets of potential and final workflow outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational workflows process biological data through multiple stages with multiple computational modules, then the completeness and coverage of output analysis is improved, but the number of outputs increases exponentially making manual evaluation infeasible
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the computational workflow outputs and user evaluation. The model automatically processes and ranks the exponential number of outputs, serving as a mediator that handles the evaluation task that is infeasible for manual processing while maintaining comprehensive analysis across all outputs.
Solution Approach 2:
The system enables self-service by having the machine learning model autonomously evaluate and rank all workflow outputs without requiring manual intervention. The model independently processes each output, assigns confidence values, and prioritizes results, allowing the system to serve itself in the evaluation task rather than relying on human users to manually assess each output.
2Measurement precision
If users manually evaluate each output from computational workflow, then the accuracy of output selection is improved, but the time and effort required becomes prohibitive
Solution Approach 1:
The patent replaces the mechanical process of manual human evaluation with an automated machine learning system. Instead of users manually examining and evaluating each output, the machine learning model performs the evaluation automatically, substituting the mechanical human effort with an automated computational process that maintains accuracy while eliminating time loss.
3Manufacturing precision
If confidence values are determined for all potential workflow outputs, then the quality of output ranking is improved, but the computational resources required increase
Solution Approach 1:
The patent applies partial action by having the machine learning model process outputs in a prioritized manner. Rather than uniformly processing all outputs with equal computational intensity, the model focuses computational resources on evaluating outputs that are more likely to be relevant or important, assigning confidence values strategically to achieve quality ranking while managing computational resource consumption efficiently.
Data Source
AI summary
System and method for training a machine learning model to determine confidence values for potential workflow outputs generated by a computational workflow and/or determining confidence values for potential workflow outputs using a trained machine learning model. Exemplary implementations may: store a workflow definition defining a computational workflow, computational modules, final workflow outputs, sets of potential workflow outputs, and/or other information; obtain the sets of potential workflow outputs and final workflow outputs for multiple ones of the input information sets; compile the sets of potential workflow outputs and final workflow outputs into input/output pairs for the corresponding input information sets; train a machine learning model based on the input/output pairs for the multiple input information sets to generate a trained machine learning model and/or other information; and/or other exemplary implementations.