Segment-wise Input Feature Representation for Genomics Data Analysis
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Solution Overview
Problem
Existing predictive data analysis systems face challenges in efficiently processing large and data-intensive datasets, particularly in genomics, due to the massive size of genetic data and hardware/software limitations, making it difficult to represent genetic variants consistently for ingestion by Deep Learning algorithms.
Innovation Solution
The approach involves converting large datasets into input feature representation super-segments and segments, using a transformer-based language model to generate multi-segment input feature representations, and performing predictive data analysis by segmenting input spaces hierarchically, enabling faster and less resource-intensive processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large genomics datasets are processed using traditional Deep Learning algorithms, then predictive data analysis can be performed, but hardware resources and processing time become excessively large
Solution Approach 1:
The patent segments the input feature representation into multiple segments (e.g., chromosomal segments) and processes each segment independently through a shared embedding model. This segmentation reduces the computational burden on hardware by breaking down large datasets into manageable units, thereby resolving the contradiction between performing predictive analysis on large genomics data and avoiding excessive hardware resource consumption.
2Productivity
If large genomics datasets are processed using traditional Deep Learning algorithms, then predictive data analysis can be performed, but processing time becomes excessively large
Solution Approach 1:
By dividing the input feature representation into chromosomal segments and processing them in parallel through the shared embedding model, the patent significantly reduces processing time. The segmentation enables concurrent computation on multiple data portions, thereby resolving the contradiction between performing predictive analysis and minimizing processing time.
Solution Approach 2:
The shared embedding model serves multiple functions by processing different chromosomal segments through a single unified model architecture. This multi-functionality reduces the need for multiple separate model instances, thereby decreasing processing time while maintaining predictive analysis capability on large genomics datasets.
3Reliability
If genetic variants are represented consistently for Deep Learning ingestion, then predictive analysis can be performed, but device complexity increases
Solution Approach 1:
The patent segments the genetic data into chromosomal segments, each processed by the shared embedding model to generate consistent representations. This segmentation approach simplifies the overall processing system by using a single model architecture for all segments, thereby resolving the contradiction between achieving consistent genetic variant representation and avoiding increased device complexity.
Solution Approach 2:
The shared embedding model acts as an intermediary that transforms diverse genetic variant data into consistent representations. By using a single intermediary model for all chromosomal segments, the patent maintains representation consistency while avoiding the complexity of multiple specialized models, thereby resolving the contradiction between reliable representation and system complexity.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing health-related predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using at least one of shared segment embedding machine learning models or transformer-based machine learning models.