A Bibliographic Platform for Biomedical Literature Enriched by AI.

Unlike search engines such as PubMed, which primarily index keywords in abstracts, we've developed AI models to detect specific biomedical concepts and determine their potential interactions.
Thanks to these advanced models, we have constructed a massive network of interaction between biomedical concepts, comprising more than 500 million links. This platform is the direct result of our efforts to provide you with an intuitive interface to query this immense network.

Why is Quaesia More Powerful?

Enhanced Query Building
Our search engine is superior to conventional tools because it allows for more precise and comprehensive queries based on established biomedical concepts.
  • MeSH Thesaurus Integration: You define your study using biomedical concepts rather than just keywords. By integrating the MeSH thesaurus (Medical Subject Headings), the system automatically accounts for the various names and acronyms of a biological entity.
  • Infinite Precision: You can combine MeSH terms in countless ways to create new, highly precise composite entities.
  • Weighted Searches: Use weighting factors to specify the importance of different entities within your query.
  • Keyword Fallback: If you can't find a MeSH concept that perfectly matches your idea, you can still use keywords, just like in a regular search engine.
Advanced Ranking and Results
  • Once you define your study's entities, our search engine ranks the 40 million abstracts based on the biological entities and keywords you specified.
  • Evidence-Based Context: You can simply click on a link within your interaction network to retrieve the most relevant article sentences that explain the relationship between two entities, all within the context of your specific query.
  • Experimental Filters: We've added modules to filter sentences based on 39 different types of relationships (e.g., increase, advantage, molecular interaction, long-range interaction, etc.).
AI-Driven Prediction Module
Leveraging our enormous relationship graph, we trained an AI model to predict potential unknown links.
  • Custom Prediction: You can select from a list of predefined targets or build your own list from the MeSH tree to find which MeSH targets are most relevant to your study.
  • Evidence Included: Each prediction includes a link to the original supporting sentences as evidence available in our database, which potentially explain the predicted relationship.
AI-Augmented Abstract & Author Information
  • Abstract Highlighting: Thanks to manual annotations, we can significantly accelerate your reading of an abstract by clearly highlighting the entities relevant to your specific study.
  • Entity Discovery: Other identified entities are underlined. A simple click allows you to add new entities to your study, further refining your focus.
  • Relationship Visualization: We also display a graph of the relationships between biomedical entities, enabling you to access relevant supporting sentences with a single click.
  • Author Profile: The dedicated author page features three insightful histograms: one detailing the author's publications, one showing publications that cite the author, and one displaying all references utilized. Additionally, a table provides a complete list of MeSH terms used by the author.
Help Improve Our Models
We've developed a set of tools that allow you to help us correct errors made by the AI systems, enabling us to build even better models.
Your Personalized Data
  • Study Preservation: Once you connect using a LinkedIn or Patreon account, you can save your studies and lists of articles.
  • Annotation History: You can also review all the annotations you have personally added to help correct and improve our automated AI systems.
Support and Sustainability
The database currently contains approximately 40 million abstracts of publications referenced on PubMed and will integrate 1.5 million new ones each year.
  • Cost of Curation: It is time-consuming and expensive to automatically annotate all these texts using our AI models. Despite extensive optimizations, the initial calculation took several months on a GPU-equipped server, and it will take more than three months of annual calculation just to keep the database up to date.
  • Funding: To finance this platform, we created a Patreon page: www.patreon.com/Quaesia.
  • Your Impact: With your generous donations, we can continue to maintain this platform and develop new, cutting-edge tools for the entire scientific community.
Study Description
AI-Predicted Interactions
Build your study, and then select a MeSH target buttons
Wait...
AI-augmented abstract
Publications:
Cited by:
References:
MeSH Usage by Author
Color MeSH #PMID
Tutorials