This project aims to develop a tool leveraging large language models (LLMs) to recommend bioinformatics APIs and detect API usage errors in research code. The tool will specifically address the challenges of proper API usage in bioinformatics, where incorrect data handling can lead to faulty conclusions and, in severe cases, affect patient outcomes. The LLM will be trained on bioinformatics codebases, ensuring the tool can understand and guide correct API usage. The end goal is to improve research reliability, accelerate discoveries, and enhance data-driven health solutions.
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LLM Training Data: A comprehensive dataset of bioinformatics code and API documentation is required to train the model.
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API Integration: Access to public genomic databases and bioinformatics tools like NCBI, Ensembl, and others.
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Error Detection Algorithms: Implementation of real-time code analysis for API usage, including pattern recognition and corrective feedback.
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User Interface: A user-friendly interface for researchers to interact with the tool, receive suggestions, and view error corrections.
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Compliance and Security: Ensuring all interactions are compliant with data security standards, including HIPAA and GDPR for sensitive genomic data.
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Scalability Infrastructure: Cloud or on-premise infrastructure capable of handling large-scale datasets and computationally intensive bioinformatics workflows.