Genomic Context Embedding via Protein Language Models
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Solution Overview
Problem
Conventional techniques for modeling genomic information fail to accurately and reliably represent genes in their contextual relationships due to limitations in capturing multidimensional properties like phylogeny, structure, and function, and lack generalizability by considering genes independently and focusing on short genomic segments from narrow lineages.
Innovation Solution
A method involving obtaining genomic context information, encoding it to obtain an initial encoding, and processing this encoding with a genomic language model to generate contextual embeddings of genes, which accounts for multiple genes within a genomic context and represents them in continuous space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques model genes independently using short genomic segments, then the modeling process is simple and computationally efficient, but the accuracy and reliability of gene representation deteriorates due to failure to capture contextual relationships
Solution Approach 1:
The patent segments the genomic context into multiple discrete genes within a defined genomic region (e.g., 5-50 genes). Each gene is processed individually through the embedding system while maintaining their contextual relationships, allowing the model to capture local genomic structures without overwhelming computational complexity
Solution Approach 2:
The patent implements a nested structure where protein sequence embeddings are generated first, then gene embeddings are created by aggregating protein embeddings, and finally contextual embeddings are generated by incorporating neighboring gene information. This hierarchical nesting allows complex relationships to be built from simpler components
2Adaptability or versatility
If conventional techniques focus on narrow lineages and short genomic segments, then the data processing is manageable, but the generalizability of the model deteriorates
Solution Approach 1:
The patent creates a universal embedding system that processes genes from diverse lineages using the same architectural framework. The model handles different organism types and genomic contexts through a unified approach, enabling broad generalizability across biological systems while managing data through standardized processing pipelines
Solution Approach 2:
The patent transforms genomic data from traditional sequence space into continuous embedding space, adding dimensional depth to represent phylogenetic, structural, and functional properties simultaneously. This dimensional transformation allows the model to capture complex relationships without requiring exponentially more data
3Loss of information
If conventional techniques use simple gene models, then the computational resources required are minimal, but the ability to capture multidimensional properties (phylogeny, structure, function) deteriorates
Solution Approach 1:
The patent pre-computes and stores protein sequence embeddings for each gene before contextual analysis. This preliminary action captures structural and functional properties in advance, so that during contextual modeling, only aggregation and integration operations are needed, reducing real-time computational burden while preserving multidimensional information
Solution Approach 2:
The patent introduces protein embeddings as an intermediary representation between raw gene sequences and final contextual embeddings. This intermediary layer efficiently encodes multidimensional properties (phylogeny, structure, function) in a compressed form that can be aggregated without requiring direct processing of all raw genomic data
Data Source
AI summary
Some aspects provide for a method for generating a contextual embedding of a gene. In some embodiments, the method comprises: using at least one computer hardware processor to perform: obtaining information specifying genomic context of the gene, the genomic context containing a plurality of genes including the gene, the information containing gene sequences for the plurality of genes; encoding the information specifying the genomic context to obtain an initial encoding of the genomic context, the encoding comprising: mapping the gene sequences to protein sequences; and encoding the protein sequences using a trained protein language model (pLM) to obtain the initial encoding of the genomic context; and processing the initial encoding of the genomic context with a genomic language model (gLM) to obtain the contextual embedding of the gene.