MOGONET provides more holistic view of biological processes underlying
disease
Novel multi-omics data analysis AI framework outperforms existing methods


Date:
August 26, 2021
Source:
Regenstrief Institute
Summary:
To fully utilize the advances in omics technologies to achieve
a more comprehensive understanding of the biological processes
underlying human diseases, researchers have developed and
tested MOGONET, a novel multi- omics data analysis algorithm and
computational methodology. Integrating data from various omics
provides a more holistic view of biological processes underlying
human diseases. The creators have made MOGONET open source, free
and accessible to all researchers.



FULL STORY ========================================================================== Genomics, proteomics, metabolomics, transcriptomics -- rapid advances in
high- throughput biomedical technologies has enabled the collection of
data with unprecedented detail from the growing number of omics. But, how
best to take advantage of the interactions and complementary information
in omics data?

==========================================================================
To fully utilize the advances in omics technologies to achieve a more comprehensive understanding of the biological processes underlying human diseases, researchers from Regenstrief Institute and Indiana, Purdue and
Tulane Universities have developed and tested MOGONET, a novel multi-omics
data analysis algorithm and computational methodology. Integrating data
from various omics provides a more holistic view of biological processes underlying human diseases. The creators have made MOGONET open source,
free and accessible to all researchers.

In a study published in Nature Communications, the scientists demonstrated
that MOGONET, short for Multi-Omics Graph cOnvolutional NETworks,
outperforms existing supervised multi-omics integrative analysis
approaches of different biomedical classification applications using
mRNA expression data, DNA methylation data, and microRNA expression data.

They also determined that MOGONET can identify important omics signatures
and biomarkers from different omics data types.

"With MOGONET, our new AI [artificial intelligence] tool, we employ
machine learning based on a neural network, to capture complex biological process relationships. We have made the understanding of omics more comprehensive and also are learning more about disease subtypes that
biomarkers help us differentiate," said Regenstrief Institute Research Scientist Kun Huang, PhD, who led the study. "The ultimate goal is to
improve disease prognosis and enhance disease-outcome predictions." A bioinformatician, he credits the diversity of the MOGONET research
group, which included computer scientists as well as data scientists and bioinformaticians, with their varying perspectives, as instrumental in
its development and success. He serves as director of data sciences and informatics for the Indiana University Precision Health Initiative.

The researchers tested MOGONET on datasets related to o Alzheimer's
disease, gliomas, kidney cancer and breast invasive carcinoma as well as
on healthy patient datasets. They determined MOGONET handily outperformed existing supervised multi-omics integration methods.

"Learning and integrating intuitive recognition, MOGONET could
generate new biomarker disease candidates,"said study co-author
Regenstrief Institute Affiliated Scientist Jie Zhang, PhD, a
bioinformatician. "MOGONET also could predict new cancer subtypes, tumor
grade and disease progression. It can identify normal brain activity
versus Alzheimer's disease." Drs. Huang and Zhang plan to expand this
work beyond omics to include imaging data, noting the abundance of
brain images for AD and cancer-related pathology images which can teach
MOGONET to recognize even cases it had not previously encountered. Both scientists note that following rigorous clinical studies, MOGONET could
support improved patient care in many areas.

In addition to Drs. Huang and Zhang, authors of "MOGONET integrates
multi-omics data using graph convolutional networks allowing patient classification and biomarker identification" are Tongxin Wang, PhD, and
Haixu Tang, PhD, of Indiana University, Wei Shao, PhD, of IU School of Medicine; Zhi Huang of IU School of Medicine and Purdue University; and Zhengming Ding, PhD of Tulane University. Dr. Wang worked in Dr. Huang's laboratory. Dr. Ding, formerly of Indiana University, is an expert in
the field of machine learning.

The development and testing of MOGONET was supported by National
Institutes of Health grants R01EB025018 and U54AG065181 and the Indiana University Precision Health Initiative.

========================================================================== Story Source: Materials provided by Regenstrief_Institute. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Tongxin Wang, Wei Shao, Zhi Huang, Haixu Tang, Jie Zhang,
Zhengming Ding,
Kun Huang. MOGONET integrates multi-omics data using graph
convolutional networks allowing patient classification and biomarker
identification.

Nature Communications, 2021; 12 (1) DOI: 10.1038/s41467-021-23774-w ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210826081703.htm

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