In the era of information overload, researchers often face challenges in finding precise, evidence-based answers to their specific scientific questions.
MethodMIND solves this problem by leveraging cutting-edge technology to curate and deliver methodologically sound recommendations. This tool aims to accelerate innovation and improve the reproducibility of scientific research across diverse domains, including neuroscience, biomedical research, and more.
MethodMIND combines powerful technologies to provide accurate and context-aware recommendations: Retrieval-Augmented Generation (RAG): Ensures access to a curated, up-to-date knowledge base, linking user queries with relevant literature and methodologies. SciBERT: A specialized language model trained on scientific texts to understand and analyze complex research language and concepts effectively. Large Language Models (LLMs): Enhance the tool’s ability to provide comprehensive and coherent responses tailored to user queries.
Looking ahead, the goal is to expand MethodMIND beyond brain diseases, scaling it to support global research across multiple scientific disciplines by augmenting its database and refining the vector matching process for even greater precision.
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Demo day video
Tech stack
Python
Streamlit
Gemini
Meet the team
Elissa Cusson
Liam Watford Cendra
Jaime Planas Lee
Jean-Marc Andre
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