Genomic Variant Pathogenicity Prediction Model
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Solution Overview
Problem
Existing genomic data analysis tools face inefficiencies and inaccuracies in predicting the pathogenicity of DNA mutations due to complexity and noise in genomic data, leading to time lags and potential misinterpretation in medical records.
Innovation Solution
A computer-implemented method using a predictive model trained with annotated data to determine the pathogenicity of variants by generating genetic condition clusters and employing supervised learning frameworks to assess unknown variants based on side information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models undergo complete retraining when new input data is available, then model accuracy may be improved, but time lag and resource consumption increase significantly
Solution Approach 1:
The model is pre-trained on comprehensive genomic data and pathogenicity annotations before deployment. When new variants are queried, the pre-trained model can immediately provide predictions without requiring retraining, thus eliminating time lag while maintaining prediction accuracy through the knowledge already encoded in the model weights
Solution Approach 2:
The system dynamically adapts to new data by using the pre-trained model to generate predictions on novel variants without full retraining. The model structure remains fixed but can be incrementally updated with new predictions and annotations, allowing continuous improvement without the time penalty of complete retraining cycles
2Measurement precision
If conventional machine learning models undergo complete retraining when new input data is available, then model accuracy may be improved, but resource consumption increases significantly
Solution Approach 1:
The computationally intensive training process is performed once in advance on high-performance infrastructure before deployment. The pre-trained model is then deployed to production systems where it can make predictions with minimal computational resource consumption, avoiding the need for repeated full retraining cycles that would consume significant energy
Solution Approach 2:
The knowledge learned during the initial training phase is encoded into the model weights and parameters, creating a persistent copy of the training results. This allows the model to make predictions on new data without needing to reprocess the original training data, significantly reducing computational resource consumption while maintaining prediction accuracy
3Measurement precision
If genomic data analysis tools use extensive training data to improve prediction accuracy, then pathogenicity assessment precision is improved, but model complexity and training time increase
Solution Approach 1:
The training data is segmented into distinct components: genomic sequences, variant annotations, pathogenicity labels, and phenotypic information. The model architecture is correspondingly segmented into layers that process each type of information separately before integrating them, which manages complexity while utilizing extensive training data effectively
Solution Approach 2:
The model transforms the high-dimensional genomic data into a lower-dimensional latent representation space that captures the essential features for pathogenicity prediction. This dimensionality reduction maintains prediction accuracy by preserving critical information while reducing model complexity and computational burden
4Measurement precision
If genomic data analysis tools use extensive training data to improve prediction accuracy, then pathogenicity assessment precision is improved, but training time increases
Solution Approach 1:
The model performs comprehensive training on extensive genomic data and pathogenicity annotations in advance, before deployment to production systems. This preliminary training action encodes all necessary knowledge into the model weights, allowing the model to provide accurate predictions on new variants without requiring additional training time during clinical operations
Solution Approach 2:
The model is designed to continuously process and learn from new variant predictions and annotations after deployment, incrementally improving its performance over time without requiring full retraining. This continuous learning maintains high prediction accuracy while avoiding the extended training times associated with complete retraining cycles
Data Source
AI summary
A computer-implemented method that is for assessing pathogenicity of a variant for a patient. Receive a variant. Determine at least one probability for the variant in relation to pathogenic metrics based on a collection of learned variants. The pathogenic metrics comprise a data representation of at least one genetic condition cluster for determining at least one probability for the variant. The combined representation of at least one probability of the variant for the patient is outputted.