BioMap is an attempt to create a scalable knowledgebase of biological relationships extracted from the vast amount of biomedical literature data. Biomap discovers associations among biological entities from literature and validates these associations.

Large number of associations among various biological entities discovered experimentally are routinely reported in biomedical literature. Associations can be direct, directional or transitive. BioMap is used to extract various associations that can be customized to specific biomedical research domains using the following methods:

• Develop direct, directional, and transitive association discovery algorithms using machine learning and NLP techniques
• Search and retrieve documents pertaining to specific problem domains
• Generate association networks
• Validate biological association networks using statistical methods
• Validate biological associations using evolutionary principles
• Apply valid associations found in specific pathways to help develop valid, testable hypotheses

Biomap has been designed and developed by Mathew J. Palakal. His research interests are biomedical literature mining and association discovery.

If you reference this work in a publication, please cite as follows:

• (2008), "BioMap: Biomedical Associations and Pathways," http://ccehub.org/resources/164.

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