AUTHOR=Abdel-Hafiz Mohamed , Najafi Mesbah , Helmi Shahab , Pratte Katherine A. , Zhuang Yonghua , Liu Weixuan , Kechris Katerina J. , Bowler Russell P. , Lange Leslie , Banaei-Kashani Farnoush TITLE=Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification JOURNAL=Frontiers in Big Data VOLUME=5 YEAR=2022 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.894632 DOI=10.3389/fdata.2022.894632 ISSN=2624-909X ABSTRACT=
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting