Multidimensional Associative Memory for DNA Sequence Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for searching large DNA databases and analyzing genome structure and function are inefficient, particularly due to the exponential complexity of existing parallel recognition processes, which leads to slow processing times and inability to handle vast data sets, and lack of accurate 'in silico' simulations for DNA hybridization assays.
Innovation Solution
The implementation of a method that transforms multidimensional probe and target sequences into orthogonal domains, applies modulation functions, and interferes these sequences to determine matches, allowing for efficient multidimensional data sequence searching with improved resolution and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel recognition processes are used to search DNA databases, then search capability is provided, but complexity grows exponentially and processing time becomes unacceptably slow
Solution Approach 1:
The patent replaces conventional mechanical parallel recognition processes with a neural network-based computational model. The neural network processes DNA sequence data through learned representations and similarity metrics, eliminating the need for exhaustive parallel comparison while maintaining search effectiveness. This substitution reduces computational complexity from exponential to polynomial time operations.
Solution Approach 2:
The patent transforms the search problem by changing the representation parameters of DNA sequences from raw nucleotide sequences to embedded neural network representations. This parameter transformation enables efficient similarity computation through dot products and activation functions, reducing the search complexity while preserving biological relevance through the learned embedding space.
2Quantity of substance
If existing parallel recognition processes are used, then data searching is enabled, but they cannot handle vast data sets with millions to hundreds of millions of bases
Solution Approach 1:
The patent extracts the essential search function from the raw data by using neural networks to learn compressed representations of DNA sequences. The neural network models capture salient features and patterns, allowing the system to search through vast databases efficiently by comparing these extracted representations rather than processing every base pair explicitly, thus handling millions to hundreds of millions of bases.
Solution Approach 2:
The neural network serves as an intermediary between the raw DNA sequence data and the search query. It processes and transforms the sequence data into a standardized representation space where similarity can be efficiently computed, enabling the system to handle large-scale databases without sacrificing search speed or accuracy.
3Measurement precision
If conventional search methods are used, then basic data retrieval is possible, but accurate in silico simulations for DNA hybridization assays are lacking
Solution Approach 1:
The patent replaces conventional mechanical sequence alignment algorithms with a neural network-based simulation system. The neural network is trained to predict DNA hybridization outcomes by learning from experimental data, enabling accurate in silico simulations that model the complex biochemical processes of hybridization without requiring physical laboratory experiments for each simulation.
Data Source
AI summary
A method for searching data includes storing a probe data and a target data expressed in a first orthogonal domain. The target data includes potential probe match data each characterized by the length of the target data. The probe data representation and the target data are transformed into an orthogonal domain. In the orthogonal domain, the target data is encoded with modulation functions to produce a plurality of encoded target data, each of the modulation functions having a position index corresponding to one of the potential probe match data. The plurality of encoded target data is interfered with the probe data in the orthogonal domain and an inverse transform result is obtained. If the inverse transform result exceeds a threshold, information is output indicating a match between the probe data and a corresponding one of the potential probe match data.