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Best AI Links (for Biotechnology & Research)
Best AI Links (for Biotechnology & Research)
This section highlights leading-edge AI tools and resources that are transforming biological research, from protein folding to data analysis.
This section highlights leading-edge AI tools and resources that are transforming biological research, from protein folding to data analysis.
Developed by DeepMind, AlphaFold is a revolutionary AI system that predicts a protein's 3D structure from its amino acid sequence. This database provides open access to millions of structure predictions.
While known for language models, Hugging Face is also a hub for specialized AI models, including those for scientific text classification, protein sequence analysis (e.g., ProteinBERT), and more. A great place to explore pre-trained AI models.
A collection of research that uses TensorFlow, Google's open-source machine learning framework, for biological applications. Useful for those interested in building their own AI models.
A leading open-source machine learning framework favored by many academic researchers for its flexibility and ease of use in building custom AI models.
An AI-powered tool to help researchers and scientists find and explore academic papers. Enter a paper, and it generates a graph of related papers, helping you discover seminal and recent works in a field.
Elicit uses language models to help automate research workflows. You can ask a research question, and it will find relevant papers and summarize key takeaways, helping to accelerate literature reviews.
An AI-powered research and writing assistant from Google. Unlike general-purpose chatbots, NotebookLM is grounded in the documents you provide. You can upload your research papers, lab notes, project proposals, and other source materials, and it becomes an expert in your information. Use it to summarize documents, find specific data points, brainstorm ideas, create outlines, and generate new text based solely on your trusted sources. It's an exceptional tool for accelerating literature reviews, analyzing qualitative data, and synthesizing information from your own research library.
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