# Knowledge-Based Biomedical Data Science

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2020 · $517,554

## Abstract

Knowledge-based biomedical data science
 In the previous funding period, we designed and constructed breakthrough methods for creating a
semantically coherent and logically consistent knowledge-base by automatically transforming and
integrating many biomedical databases, and by directly extracting information from the literature.
Building on decades of work in biomedical ontology development, and exploiting the architectures
supporting the Semantic Web, we have demonstrated methods that allow effective querying spanning
any combination of data sources in purely biological terms, without the queries having to reflect
anything about the structure or distribution of information among any of the sources. These methods
are also capable of representing apparently conflicting information in a logically consistent manner, and
tracking the provenance of all assertions in the knowledge-base. Perhaps the most important feature of
these methods is that they scale to potentially include nearly all knowledge of molecular biology.
 We now hypothesize that using these technologies we can build knowledge-bases with broad enough
coverage to overcome the “brittleness” problems that stymied previous approaches to symbolic artificial
intelligence, and then create novel computational methods which leverage that knowledge to provide
critical new tools for the interpretation and analysis of biomedical data. To test this hypothesis, we
propose to address the following specific aims:
 1. Identify representative and significant analytical needs in knowledge-based data science, and
 refine and extend our knowledge-base to address those needs in three distinct domains: clinical
 pharmacology, cardiovascular disease and rare genetic disease.
 2. Develop novel and implement existing symbolic, statistical, network-based, machine learning
 and hybrid approaches to goal-driven inference from very large knowledge-bases. Create a goal-
 directed framework for selecting and combining these inference methods to address particular
 analytical problems.
 3. Overcome barriers to broad external adoption of developed methods by analyzing their
 computational complexity, optimizing performance of knowledge-based querying and inference,
 developing simplified, biology-focused query languages, lightweight packaging of knowledge
 resources and systems, and addressing issues of licensing and data redistribution.

## Key facts

- **NIH application ID:** 9955351
- **Project number:** 5R01LM008111-15
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** LAWRENCE E HUNTER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $517,554
- **Award type:** 5
- **Project period:** 2004-09-30 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9955351

## Citation

> US National Institutes of Health, RePORTER application 9955351, Knowledge-Based Biomedical Data Science (5R01LM008111-15). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9955351. Licensed CC0.

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