Gene Prioritization With Literature-Validated LLM Profiling
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Solution Overview
Problem
Traditional methods for interpreting large-scale gene profiling data lack nuanced biological context and fail to systematically validate LLM predictions, leading to inefficiencies in identifying therapeutic targets.
Innovation Solution
A two-stage computational framework combining LLM-based screening with literature validation for systematic gene prioritization, using a multi-criteria evaluation and retrieval-augmented generation to align LLM predictions with literature evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLM-based screening is used for gene prioritization, then processing efficiency and speed are improved, but reliability and accuracy deteriorate due to hallucination and lack of systematic validation
Solution Approach 1:
The patent introduces an intermediary validation layer between the LLM screening and final gene prioritization. This intermediary component systematically validates LLM predictions against multiple data sources including literature databases, experimental data, and expert knowledge, thereby maintaining the high processing efficiency of LLMs while improving reliability through structured verification
Solution Approach 2:
The patent implements a feedback mechanism where LLM predictions are systematically validated and the results are fed back to improve future predictions. The validation process includes cross-checking against known gene databases, literature evidence, and experimental data, creating a closed-loop system that continuously improves accuracy while maintaining efficiency
2Ease of operation
If traditional ontology or pathway enrichment methods are used, then ease of operation is improved, but measurement precision and biological context understanding deteriorate
Solution Approach 1:
The patent combines multiple information sources and validation methods into a composite analytical framework. It integrates ontology-based approaches with literature mining, experimental data, and expert knowledge systems, creating a multi-layered validation structure that maintains operational simplicity while dramatically improving biological context understanding through diverse data integration
3Reliability
If comprehensive literature validation is performed, then reliability is improved, but loss of time and processing duration worsen
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing validation data sources before the actual gene prioritization task. Literature databases, experimental data, and expert knowledge are pre-structured and indexed, enabling rapid validation during the actual prioritization process without sacrificing comprehensive validation coverage
Solution Approach 2:
The patent segments the validation process into multiple independent modules that can operate in parallel. Different validation checks (literature search, database cross-reference, expert knowledge verification) are divided into separate computational modules, reducing overall validation time through concurrent processing while maintaining comprehensive reliability checks
Data Source
AI summary
The present disclosure relates to a multi-phase method for determining a set of candidate genes. During a first phase, the method includes prompting a naïve language model with a plurality of prompts corresponding to a plurality of candidate genes to generate a set of initial scores indicative of each corresponding candidate gene's potential as a biomarker or therapeutic target. During a second phase, the method includes determining, for each candidate gene, a set of relevant documents from a curated document library. The method also includes prompting a further language model using the relevant documents to generate secondary scores. During a third phase, the method includes determining, for each candidate gene, at least one of: a decision classification, a recalibrated score, and a detailed scientific explanation. The method includes determining a final candidate set and conducting a multi-dimensional optimization analysis on each candidate gene of the final candidate set.