Phylogenetic Tree Vector Representations for Protein Sequence Prediction
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Solution Overview
Problem
Current bioinformatics techniques face challenges in accurately predicting nucleic acid or protein sequences and inferring evolutionary relationships using multi-sequence alignments and phylogenetic trees, as they lack effective methods for generating predictive models from phylogenetic data.
Innovation Solution
A computer-implemented method that creates a multi-sequence alignment and generates a phylogenetic tree, which is then used to train machine learning models to produce vector representations of nucleic acid or protein sequences, enabling the prediction of evolution, regression, and sibling sequences based on the phylogenetic tree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bioinformatics techniques are used to predict nucleic acid or protein sequences from multi-sequence alignments and phylogenetic trees, then the methods are computationally feasible, but the prediction accuracy and ability to infer evolutionary relationships are insufficient
Solution Approach 1:
The patent introduces vector representations as an intermediary between the phylogenetic tree data and the sequence prediction task. The machine learning models first convert phylogenetic tree data into vector representations, which then serve as input for predicting nucleic acid or protein sequences. This intermediary representation enables more accurate capture of evolutionary relationships while maintaining computational feasibility.
Solution Approach 2:
The patent transforms the structural phylogenetic tree data into continuous vector space representations, changing the parameter space from discrete tree structures to continuous vectors. This parameter transformation allows the application of sophisticated machine learning models that can capture nuanced evolutionary relationships, thereby improving prediction accuracy without prohibitive computational cost.
2Reliability
If sophisticated machine learning models are employed to generate vector representations from phylogenetic trees, then the predictive capability for evolutionary relationships improves, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the complex task of sequence prediction into distinct components: (1) generating vector representations from phylogenetic trees, (2) using these vectors to predict sequences, and (3) evaluating evolutionary relationships. This segmentation allows each component to be optimized independently, improving reliability while managing computational resources more efficiently.
3Loss of information
If deep generative modeling is used to learn sequences from phylogenetic data, then the understanding of structural properties and evolutionary relationships is enhanced, but the complexity of the analysis pipeline increases
Solution Approach 1:
The patent replaces traditional mechanical bioinformatics analysis pipelines with data-driven machine learning models. Instead of using conventional sequence alignment and phylogenetic analysis methods, the system uses neural network-based generative models that automatically learn evolutionary patterns from data, thereby reducing information loss while managing pipeline complexity through automated feature learning.
Data Source
AI summary
Generative modeling from phylogenetic data is provided. The method comprises creating a multi-sequence alignment (MSA) based on a nucleic acid or protein sequence and generating a phylogenetic tree based on the MSA. The phylogenetic tree is fed into a number of machine learning models, which generate vector representations of the nucleic acid or protein sequences based on the phylogenetic tree. The machine learning models generate from the vector representation predicted nucleic acid or protein sequences for at least one of an evolution sequence, regression sequence, or sibling sequences of nucleic acids or proteins according to the phylogenetic tree.