Multimodal Biological Sequence Analysis With Natural-Language Prompts
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Solution Overview
Problem
Existing machine learning technologies in healthcare and biomedical research require expertise and are challenging to implement without coding, and conventional models are tailored for single tasks, leading to a proliferation of individual models that are difficult to integrate for complex biological pathway analysis.
Innovation Solution
A unified, natural-language-based framework that combines biological sequence data with conversational prompts, allowing complex analysis tasks like sequence modifications and property quantification, using a machine learning model for input and output in a text-based format, facilitating user interaction and transfer learning across tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual models are created for each specific task, then task-specific performance is improved, but device complexity and difficulty of integration increase
Solution Approach 1:
The patent applies universality by creating a single multimodal model that can perform multiple biological sequence analysis tasks (gene prediction, variant detection, regulatory element identification, protein structure prediction, etc.) rather than requiring separate specialized models for each task. This unified model architecture processes different biological data types through shared components while maintaining task-specific performance through specialized processing layers.
Solution Approach 2:
The patent merges multiple individual models into one integrated multimodal system that combines processing capabilities for different biological sequence types (DNA, RNA, protein) and data modalities (sequences, structures, expressions) within a single unified architecture, thereby reducing the number of separate models while maintaining comprehensive analytical capabilities.
2Reliability
If conventional models are used for analysis, then coding expertise is required for implementation, but ease of operation decreases
Solution Approach 1:
The patent introduces a natural language interface as an intermediary between users and the complex underlying machine learning models. This interface layer translates user-friendly biological questions into appropriate model queries and formats results into comprehensible responses, eliminating the need for users to write or understand complex coding while maintaining full access to sophisticated analytical capabilities.
3Device complexity
If a unified model is used for multiple tasks, then device complexity is reduced, but training efficiency and accuracy may worsen
Solution Approach 1:
The patent applies segmentation by dividing the unified multimodal model into distinct processing modules for different biological data types (sequence processing module, structure processing module, expression processing module) while maintaining a shared foundation. This modular architecture enables independent training and optimization of each module while preserving overall system efficiency and accuracy through shared learned representations.
Data Source
AI summary
Provided herein are technologies for framing and evaluating biological sequence-based analysis tasks in a unified, natural-language-based, text in and text out format. Among other things, methods and systems of the present disclosure provide machine-learning technologies for combining biological sequence data, representing, for example, DNA, RNA, and protein sequences, with natural language, conversational style prompts that set out particular analysis tasks to be performed on the biological sequence data. This approach, for example, allows complex analysis tasks, including, but not limited to, identification of various sequence modifications, genes, and regulatory elements in DNA sequences, and quantification of properties such as degradation propensity of RNA and protein stability, to be input to a machine learning model in a uniform text-based format and for output to be generated in a same, unified, text-based format.