Looking for Hidden Gems in Scientific Literature
10 days ago
- #AI in Science
- #Scientific Research
- #Literature-Based Discovery
- Literature-based discovery (LBD) aims to uncover hidden connections in scientific literature that are not explicitly linked.
- Don Swanson pioneered LBD by identifying links between magnesium deprivation and migraines, demonstrating the potential of connecting disjointed research.
- Scientific literature contains 'undiscovered public knowledge'—ideas that exist but are forgotten or overlooked due to poor visibility, language barriers, or being ahead of their time.
- LBD methods have evolved from manual screening to advanced computational techniques, including natural language processing, machine learning, and large language models (LLMs).
- LLMs enhance LBD by enabling natural language reasoning, but they face challenges like hallucination and require human validation for reliable outputs.
- Evaluation of LBD remains a challenge due to the lack of scalable benchmarks and the subjective nature of defining a 'discovery.'
- Despite its potential, LBD has seen limited real-world applications, with notable successes in drug repurposing, such as baricitinib for COVID-19.
- LLMs exhibit combinatorial and exploratory creativity but have yet to achieve transformative creativity, which requires conceptual leaps beyond existing knowledge.
- Proposed 'daydreaming loop' algorithms could automate LBD by continuously generating and critiquing hypotheses, though computational costs remain high.
- Early LBD methods included lexical statistics and semantic models like word2vec, while modern approaches use knowledge graphs and graph neural networks for deeper insights.