# COMBINI: connecting COmplementary Medicine evidence and BIological kNowledge to support Integrative Health

> **NIH NIH U01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2024 · $671,176

## Abstract

Project Summary
Complementary medicine (CM) approaches are increasingly used by health care consumers, accepted by the
medical community, and viewed as a cornerstone of whole person health. However, much about the
effectiveness and safety of CM approaches, as well as the mechanisms through which they affect human health
and well-being, remain poorly understood. Published literature is a growing source of evidence on CM
approaches, their effect on human health, and their biological mechanisms. However, much of this evidence
remains in unstructured text and specialty journals. Furthermore, the quality of this evidence is often questioned.
The size, growth, and the quality of the literature makes it difficult for researchers and clinicians to access reliable
evidence on these topics. Concurrently, the number of curated databases on CM is growing, but they remain
limited to relatively narrow subtopics. Comprehensive resources and tools focusing on CM approaches are
currently lacking. For systematic use of the high-quality evidence on these topics for medical discovery and
patient care and effective integration of CM approaches with conventional medicine, scalable methods to distill,
standardize, and aggregate knowledge from disparate research literatures (e.g., CM, human metabolism,
microbiome, immunology) and curated databases are needed. We hypothesize that informatics approaches, in
particular natural language processing (NLP) combined with ontologies and knowledge graphs (KGs), can
underpin such consolidation and integration. In this project, we aim to develop and validate comprehensive
knowledge resources and NLP methods for mining the literature on CM approaches including their mechanisms
of biological action (which we dub COMB literature). We will integrate the mined information with knowledge from
curated databases in a KG to support knowledge management and hypothesis generation applications.
Specifically, we aim to: (1) construct informatics resources to support COMB-related knowledge
integration and extraction; (2) develop NLP methods to mine COMB knowledge from biomedical
literature; (3) construct a COMB knowledge graph from literature and curated databases and
demonstrate its utility for question answering and hypothesis generation. The successful completion of
this project will deliver a comprehensive ontology of CM interventions and their biological mechanisms, the first
annotated corpus broadly focusing on CM approaches, novel NLP models, and an integrative KG on CM
approaches and their effects on human health. Furthermore, validation of these resources and tools on real-
world CM questions by domain experts will demonstrate their potential for patient care and scientific discovery.
We anticipate that the KG can be integrated with other biomedical knowledge bases and with evidence generated
in omics studies (e.g., metagenomics, metabolomics) as well as clinical data (e.g., electronic health records),
bringing us closer to a more compl...

## Key facts

- **NIH application ID:** 10941569
- **Project number:** 1U01AT012871-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Halil Kilicoglu
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $671,176
- **Award type:** 1
- **Project period:** 2024-09-17 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10941569

## Citation

> US National Institutes of Health, RePORTER application 10941569, COMBINI: connecting COmplementary Medicine evidence and BIological kNowledge to support Integrative Health (1U01AT012871-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10941569. Licensed CC0.

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