# Distance-based Panomic Analytics for Microbiome Data

> **NIH NIH R01** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2021 · $321,864

## Abstract

PROJECT SUMMARY
Our ability to study the microbiomes is enabled by the same technologies that allow us to quantify the host
physiological state at greater depth and precision, including advanced high-throughput sequencing for genomics
and transcriptomics; mass spectrometry for metabolomics, proteomics, and lipidomics; and flow-cytometry for
characterization of circulating cell populations. The integration of host and microbiome panomic data is the
roadmap for future biomedical discoveries. One example of such studies is the Integrative Human Microbiome
Project (iHMP), which is currently generating panomic data on microbes and their host environment in three
different diseases (diabetes, irritable bowel disease, pre-term delivery). Lack of appropriate analytics for these
data and a steep curve for their validation and adoption is a major concern for the community. The main challenge
of inference in panomic-scale microbiome datasets is overcoming the ‘curses of dimensionality’. Local causal
learning has proven useful for making discoveries with high-dimensional data, while distance-based learning is
a promising paradigm for multivariate data analysis. We are proposing to combine these to develop the next
generation of panomic data analytics and make these tools available directly to the biomedical investigators. The
aims of this project are: (1) Develop analytics for distance-based omnibus panomic integration; (2) Develop
methodology for top-down distance-based sub-system interdependence learning. The overarching goal is to
develop user-facing applications utilizing the methodologies in Aims 1 and 2 and apply those in several existing
studies generating panomic data. The analytics, applications, and educational resources (case studies and
tutorials) resulting from this project will enable the biomedical community to study panomic-scale datasets in a
coherent and comprehensive way. The methods and tools resulting from this project will support new biomedical
discoveries.
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## Key facts

- **NIH application ID:** 10138020
- **Project number:** 5R01LM012517-04
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Alexander V Alekseyenko
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $321,864
- **Award type:** 5
- **Project period:** 2018-06-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10138020

## Citation

> US National Institutes of Health, RePORTER application 10138020, Distance-based Panomic Analytics for Microbiome Data (5R01LM012517-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10138020. Licensed CC0.

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