Advanced Bioinformatic and Multiomics in OA Research
Concurrent Session 11
Time: 8:45 AM to 10:15 AM
Description
| Moderators |
Mohit Kapoor Anca Maglaviceanu |
| 8:45 AM - 9:25 AM |
Applying Multi-omic Strategy and Machine Learning Approaches in OA Research Jason Rockel |
| 9:25 AM - 9:35 AM |
109: SPATIOTEMPORAL METHYLOMIC RISK SIGNATURES OF OSTEOARTHRITIS IN HUMAN HIP DEVELOPMENT AND DISEASE Sarah Rice |
| 9:35 AM - 9:45 AM |
110: GENOME-WIDE ASSOCIATION STUDY (GWAS) META-ANALYSIS OF POST-TRAUMATIC OSTEOARTHRITIS OF THE KNEE: PRIMARY RESULTS FROM THE GO-PTOA GROUP Fiona Watt |
| 9:45 AM - 9:55 AM |
111: MICROBIOTA MAINTAINS MICROINFLAMMATORY HOMEOSTASIS THROUGH LOW-DOSE OF LIPOPOLYSACCHARIDE TO PROMOTE THE OSTEOGENIC EFFECTS OF NEUTROPHILS Wei Liu |
| 9:55 AM - 10:05 AM |
112: GENETIC VARIANTS ASSOCIATED WITH OA-FREE HIP SHAPE AND THEIR POTENTIAL ROLE IN FUTURE HIP OSTEOARTHRITIS RISK: FINDINGS FROM A GENOME-WIDE ASSOCIATION STUDY Yahong Wu |
| 10:05 AM - 10:07AM |
113: A MULTI-TISSUE MODEL TO INVESTIGATE SYNOVIAL MEMBRANE AND CARTILAGE CROSSTALK Carlo Alberto Paggi |
| 10:07 AM – 10:09 AM |
114: PROTEOMIC IDENTIFICATION OF PUTATIVE SENESCENCE BIOMARKERS IN HUMAN ARTICULAR CHONDROCYTES Abby Louise Brumwell |
| 10:09 AM – 10:11 AM |
115: INTERACTION BETWEEN SYNOVIUM AND SYNOVIAL FLUID MAY INDUCE FIBRINOLYTIC ACTIVITY IN OA KNEE JOINTS Nobuho Tanaka |
| 10:11 AM – 10:13 AM |
116: SPATIALLY DIRECTED TRIZONAL MENISCUS SCAFFOLD USING REGION-SPECIFIC PORCINE MENISCUS EXTRACELLULAR MATRIX CROSSLINKING ON DEMINERALIZED BONE MATRIX Chae-Won Yun |
| 10:13 AM – 10:15 AM | Q & A |
Applying Multi-omic Strategy and Machine Learning Approaches in OA Research
DescriptionThis presentation will focus on integration of data generated from multiple omic technologies, with an emphasis on consolidating metabolomics, miRNomics, and transcriptomic data. Bioinformatic approaches used to identify putative spatial communications between cell types within OA tissue using a combination of transcriptomic approaches will be described. The presentation will also include a discussion of a deep-learning approach to uncover novel, biologically-relevant endotypes from multi-omic data. Finally, this presentation will highlight a machine-learning approach for improving classification outcomes of knee OA surgery using multi-omic derived patient endotypes.
Speakers