Recurrent Autoencoder for 3D Chromatin Structure Prediction
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Solution Overview
Problem
Current methods for inferring the 3D structure of genomes are limited by their inability to perform dynamic analysis and account for time-course information, relying on optimization-based approaches that reduce usability and do not support comprehensive reconstruction of chromatin structures at high resolution.
Innovation Solution
A computer-implemented method using an autoencoder with a structured sequence of recurrent neural network units, trained on genome interaction data to derive a 3D model from contact matrices, allowing for visualization and analysis of chromatin structures at high resolution and enabling time-dependent analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If optimization-based methods are used to reconstruct 3D chromatin structure from contact matrices, then the reconstruction can be performed with existing methods, but the methods cannot perform dynamic analysis and do not support comprehensive analysis of complete genome structure
Solution Approach 1:
The patent replaces optimization-based mathematical methods with a neural network-based computational model. The recurrent neural network automatically learns the mapping from contact matrices to 3D structures, eliminating the need for manual transfer function design and enabling dynamic analysis through sequential processing of contact matrix data over time
Solution Approach 2:
The patent transforms the static optimization problem into a dynamic learning process by using neural network parameters (weights and biases) that are trained on contact matrix data. This allows the system to adapt to different genome configurations and perform dynamic analysis by processing time-varying contact matrix inputs
2Measurement precision
If high-resolution comprehensive analysis of complete genome structure is performed, then complete genome structure can be reconstructed, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The patent divides the genome into discrete bins and processes contact matrices representing interactions between these bins. The neural network processes each bin's contact profile independently and sequentially, enabling high-resolution analysis of complete genome structure while managing computational complexity through modular processing of segmented genomic data
Solution Approach 2:
The patent transforms the high-dimensional contact matrix data into a lower-dimensional 3D spatial representation. The neural network learns to compress the complex contact matrix information into essential 3D structural features, achieving high-resolution genome structure reconstruction while reducing computational burden through dimensional transformation
3Loss of information
If existing reconstruction methods are used, then 3D structure can be inferred from contact matrix, but time-course information cannot be incorporated and dynamic behavior cannot be analyzed
Solution Approach 1:
The patent employs a recurrent neural network that inherently processes sequential data, making it suitable for time-course analysis. The network maintains internal state information across time steps, allowing it to incorporate time-varying contact matrix data and analyze dynamic chromatin structure changes while preserving usability through a unified modeling framework
Data Source
AI summary
A computer-implemented method for inferring a 3D structure of a genome is provided. The method includes providing genome interaction data and operating an autoencoder including a structured sequence of n autoencoder units, each of which including an encoder unit and a decoder unit, each of which is implemented as a recurrent neural network unit. The method includes additionally training the autoencoder by feeding all vectors of genome interaction data to the encoder units. Thereby, the training of the auto-encoder units is performed stepwise by using inner state of respective previous autoencoder units in the cascaded sequence of autoencoder units and performing backpropagation within each of the plurality of autoencoder units after all autoencoder units have processed their respective input values, and using the output values of the encoder units for deriving a 3D model for a visualization of the genome.