Deep Neural Network Variant Pathogenicity Prediction
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Solution Overview
Problem
Current methods for predicting variant pathogenicity rely heavily on sequence-based approaches, which can be limited by data scarcity and circularity, and fail to effectively capture the structural information essential for understanding protein function and variant impact.
Innovation Solution
The use of deep convolutional neural networks (DCNNs) that analyze multi-channel voxelized representations of three-dimensional (3D) protein structures to predict variant pathogenicity, allowing for the integration of structural information and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequence-based approaches are used for predicting variant pathogenicity, then the prediction process is simpler and requires less computational resources, but the prediction accuracy is limited due to data scarcity and inability to capture structural information
Solution Approach 1:
The patent transitions from 1D sequence-based analysis to 3D structural analysis by voxelizing protein structures. This dimensional expansion allows the model to capture spatial relationships and structural features that are invisible to sequence-only methods, directly improving prediction accuracy while accepting increased computational complexity.
Solution Approach 2:
The patent creates a composite representation by combining multiple data types: voxelized 3D structural data, evolutionary conservation data, and sequence data. This multi-channel composite approach integrates diverse information sources to achieve more accurate pathogenicity predictions than any single data type alone.
2Measurement precision
If 3D protein structures are used for prediction, then structural information is effectively captured improving prediction accuracy, but computational resources and data processing complexity increase
Solution Approach 1:
The patent performs preliminary voxelization of protein structures before the main prediction task. By pre-processing structural data into standardized voxel representations and pre-computing evolutionary conservation metrics, the system reduces the computational burden during actual pathogenicity prediction, making the approach more scalable.
Solution Approach 2:
The patent segments the prediction task into distinct processing stages: structural feature extraction from voxel data, evolutionary conservation analysis, and final pathogenicity classification. This segmentation allows each component to be optimized independently and processed efficiently using specialized algorithms for each data type.
3Reliability
If deep convolutional neural networks analyzing 3D structures are implemented, then the ability to differentiate benign and pathogenic variants is enhanced, but the device complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces voxelization as an intermediary representation that bridges 3D protein structures and deep learning models. This intermediate format converts complex continuous 3D data into discrete grid representations that are easier for CNNs to process, simplifying the overall system architecture while maintaining structural information.
Solution Approach 2:
The patent transforms protein structure data from continuous atomic coordinates into discrete voxel grid representations with specific resolution parameters. This parameter transformation makes the data suitable for convolutional operations and enables the use of standard deep learning architectures, reducing implementation complexity.
Data Source
AI summary
The technology disclosed describes determination of which elements of a sequence are nearest to uniformly spaced cells in a grid, where the elements have element coordinates, and the cells have dimension-wise cell indices and cell coordinates. The determination includes generating an element-to-cells mapping that maps, to each of the elements, a subset of the cells. The subset of the cells mapped to a particular element in the sequence includes a nearest cell in the grid and one or more neighborhood cells in the grid, and the nearest cell is selected based on matching element coordinates of the particular element to the cell coordinates. The determination further includes generating a cell-to-elements mapping that maps, to each of the cells, a subset of the elements, and using the cell-to-elements mapping to determine, for each of the cells, a nearest element in the sequence.