Demo & Info


Try It Out

In this alpha test of the application platform, this visual presentation provides the core value proposition for all Profiler family applications of how the term input by the user are related to other terms, including those the user may be unaware of, delivering significant improvement over NLM MEDLINE PubMed Search or Google Search

Please review the following short video to learn more and see our solution in action when the term “headache” is searched on within PUBMED.

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IMDI Platform Architecture

Sample Search Results In Google

White Papers to Understand the Tech & the Industry

Literature-Based Discovery

Literature Discovery is the science of mining, processing and analyzing text collections to discover potentially useful and interesting knowledge associations for exploratory, confirmatory and predictive researcher decision support. BioCAID’s IMDI-Profiler application platform excels at organizing both known user search concepts and hidden relationships across large repositories of scientific literatures such as the MEDLINE/PubMed library and proprietary publication collections.

Hypothesis Generation Algorithms

Emerging literature research support applications are moving towards hypothesis generation predicting unknown hidden relationships from the biomedical literatures. BioCAID’s Professional and Enterprise releases will support innovative new pair-wise, substitution, chaining, mining and predictive hypothesis algorithms for advanced research discovery and decision support profiling.

Concept Map of p53 with Relevant PubMed© Citations

p53 is expressed as a conceptualized term and associations connected to broader, narrower, synonymous, keyword, co-occurring and other relationships organized by BioCAID’s IMDI-Profiler Discovery AI and knowledge relationships defined by the National Library of Medicine’s (NLM) Unified Medical Language System© (UMLS) and Medical Subject Headings© (MeSH) MetaThesauri. Organizing and presenting research term Concepts incorporating UMLS and MeSH significantly enhances our application’s “understanding” of the meaning and relationships in the language of biomedicine and health in the MEDLINE Library.