Tunable Data Structure Training for IRES Activity Prediction
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Solution Overview
Problem
Current automated systems struggle to accurately determine the efficacy of internal ribosome entry site (IRES) sequences in RNA, which are crucial for translation initiation, due to their complex interactions with ribosomes.
Innovation Solution
A tunable data structure, including a processor and memory, is trained using a partitioned training set to predict IRES activity by iteratively retraining an activity data structure, generating predicted IRES activity values, and tuning using an error function, leveraging neural networks and nucleotide sequence data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current automated systems are used to determine IRES sequence efficacy, then the analysis process is simplified, but the prediction accuracy is insufficient
Solution Approach 1:
The patent transforms the IRES activity prediction problem by changing the parameters from simple sequence analysis to multi-dimensional features including nucleotide sequences, secondary structures, and tertiary structures. This parameter transformation enables the neural network to capture complex RNA-ribosome interactions, thereby improving prediction accuracy while managing system complexity through automated feature extraction.
Solution Approach 2:
The patent employs a composite approach by integrating multiple data types (nucleotide sequences, secondary structures, tertiary structures) into a unified training dataset. This composite data structure allows the neural network to process diverse biological information simultaneously, improving prediction accuracy without requiring separate analysis systems for each data type.
2Measurement precision
If iterative retraining with partitioned training sets is implemented, then prediction accuracy improves, but training time increases
Solution Approach 1:
The patent divides the training process into distinct phases by partitioning the training set into multiple subsets. Each subset is used for specific training iterations, allowing the model to progressively learn different aspects of IRES activity. This segmentation enables efficient use of computational resources while achieving high prediction accuracy through systematic progressive learning.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and partitioning the training data before the main training process. The training set is organized into subsets in advance, and the neural network structure is pre-configured with appropriate layers and parameters. This preliminary preparation reduces computational overhead during iterative training, mitigating the time loss despite multiple training passes.
3Reliability
If multiple structural features are incorporated into the training data, then the model captures complex RNA interactions, but data processing complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple structural features (nucleotide sequences, secondary structures, tertiary structures) through a single neural network architecture. This multi-functional system processes diverse RNA structural information uniformly, improving model reliability for capturing complex RNA-ribosome interactions while avoiding the need for separate processing pipelines for each feature type.
Data Source
AI summary
An apparatus for training a tunable data structure to predict internal ribosome entry site (IRES) activity includes at least a processor and a memory containing instructions configuring the at least a processor to assemble a training set including a plurality of nucleotide sequence data examples IRES sequences and a plurality of correlated observed IRES activity, partition the training set into at least a first section and a second section, train, using the first section at least an activity data structure to generate probable IRES activity using nucleotide sequence data, and iteratively retrain the at least an activity data structure using the second section, wherein each iteration of the iterative retraining includes generating a predicted IRES activity value using the at least an activity neural network and a nucleotide sequence data example, evaluating an error function, and tuning the activity data structure.