Biological Sequence Labeling Using Positive and Negative Examples
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Solution Overview
Problem
Existing methods for labeling biological sequences, such as homology-based and machine learning-based approaches, struggle with sequences and labels not well represented in training data, leading to inaccurate labeling and high computational overhead.
Innovation Solution
A neural network system that processes input biological sequences alongside positive and negative examples to generate scores for candidate labels, leveraging similarity-based selection of sequences to implicitly identify complex patterns and reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If homology-based or traditional machine learning methods are used for labeling biological sequences, then the approach can handle sequences well-represented in training data, but it leads to inaccurate labeling and high computational overhead for sequences not well-represented in training data
Solution Approach 1:
The patent segments the labeling task by separately identifying positive sequences (those with the candidate label) and negative sequences (those without the candidate label). This segmentation allows the model to process each type of sequence independently, improving both accuracy and computational efficiency by focusing on relevant examples rather than processing all sequences uniformly.
Solution Approach 2:
The patent performs preliminary action by pre-identifying and storing positive and negative sequence examples for each candidate label during a training phase. This preliminary preparation enables the model to quickly retrieve and compare relevant sequences during inference, reducing computational overhead while maintaining high labeling accuracy for sequences not well-represented in the original training data.
2Adaptability or versatility
If traditional machine learning approaches are used, then the system can process biological sequences, but it struggles with sequences and labels not well represented in training data leading to inaccurate labeling
Solution Approach 1:
The patent introduces an intermediary mechanism by using identified positive and negative sequences as mediators between the input sequence and the labeling decision. These intermediary sequences serve as reference points that help the model generalize to unseen sequences while maintaining accurate labeling, effectively bridging the gap between training data and new sequences.
Solution Approach 2:
The patent employs copying by creating representative copies of positive and negative sequences that capture the essential characteristics of each class. These copied sequences are then used as templates for comparison with new input sequences, enabling the model to generalize accurately to sequences not well-represented in the original training data while maintaining labeling precision.
3Measurement precision
If ensemble methods are used to improve prediction accuracy, then labeling precision may improve, but computational resources and complexity increase significantly
Solution Approach 1:
The patent extracts only the essential elements needed for accurate labeling by identifying and using only positive and negative sequence examples for each candidate label. This extraction approach achieves high prediction accuracy without the need for complex ensemble methods, thereby reducing system complexity while maintaining or improving labeling precision.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting labels for biological sequences. One of the methods includes, in response to receiving a request to identify labels associated with an input biological sequence: determining, for each of a plurality of candidate labels, a score characterizing a likelihood that the input biological sequence is associated with the candidate label. Each score is determined by identifying a plurality of positive biological sequences that are each associated with the candidate label; and processing a network input including the input biological sequence and the plurality of positive biological sequences using a neural network to generate the score characterizing the likelihood that the input biological sequence is associated with the candidate label. The method includes selecting one or more of the candidate labels as labels for the input biological sequence based on the scores.