Molecular Sequence Function Prediction Using Hidden Layer Projections
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Solution Overview
Problem
Current methods for predicting molecular sequence functions struggle to accurately account for higher-order interactions between molecular components, leading to under-determined models that fail to capture the complexity of biological interactions.
Innovation Solution
A neural network-based approach is employed, where a hidden layer matrix and weights are determined during training to project and weight molecular sequences, enabling the prediction of functions by representing sequences as arrays and applying activation functions to rectify projections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple component-based models are used to predict molecular functions, then the model complexity is low and ease of manufacture is improved, but the measurement precision and ability to capture higher-order interactions deteriorates
Solution Approach 1:
The patent segments the molecular sequence into component pieces (amino acids or nucleotides) and represents each position as a separate feature in the input array. This allows the model to process complex sequences by breaking them down into manageable positional components, enabling accurate capture of higher-order interactions while maintaining computational feasibility.
Solution Approach 2:
The patent transforms the one-dimensional molecular sequence into a two-dimensional array representation where each position is a row and each component piece is a column. This dimensional transformation enables the neural network to efficiently process sequence data and capture interactions across multiple positions simultaneously, resolving the contradiction between model simplicity and prediction accuracy.
2Measurement precision
If higher-order interactions between molecular components are captured, then the measurement precision is improved, but the device complexity and number of free parameters increases causing the problem to be under-determined
Solution Approach 1:
The patent employs a universal neural network architecture that can capture higher-order interactions through its layered structure. The hidden layers with multiple neurons provide the necessary complexity to model intricate molecular interactions, while the standardized architecture ensures the model remains manageable and can be applied to various molecular sequences without becoming under-determined.
Solution Approach 2:
The patent changes the parameter representation by using position-dependent component pieces as input features rather than trying to directly model all possible interactions. This parameter transformation allows the neural network to implicitly capture higher-order interactions through its activation functions and weight matrices, achieving high prediction accuracy without explicitly enumerating all interaction parameters.
3Ease of operation
If consensus motifs are used to describe molecular sequences, then the ease of operation is improved, but the ability to capture distributed interactions and higher-order effects deteriorates
Solution Approach 1:
The patent uses a dynamic neural network model that can adaptively learn the importance of different positions and interactions rather than relying on static consensus motifs. The trained weights and activation functions allow the model to dynamically capture distributed interactions across the sequence, preserving information about higher-order effects while maintaining ease of operation through automated training.
Data Source
AI summary
Methods and systems for predicting functions of molecular sequences, comprising: generating an array that represents a sequence of molecules; determining a projection of the sequence of molecules, wherein the determining comprises multiplying a representation of the array that represents the sequence of the molecules by a first hidden layer matrix that represents a number of possible sequence dependent functions, wherein the first hidden layer matrix is determined during training of a neural network; and determining a function of the sequence of molecules by applying a plurality of weights to a representation of the projection of the sequence of molecules, wherein the plurality of weights is determined during the training of the neural network.