Workshop 1:
Advanced Bioinformatics: Computational Methods for Knowledge Discovery Based on Multi-Omics
Summary:
The development of high throughput omics technologies has enabled accurate measurement of multiple modalities simultaneously in individual studies or multi-omics integration from different studies, and rapid data accumulation in multimodal omics including genomics, transcriptomics, proteomics, metabolomics, phenomics, radionics, and the cutting-edge 3D spatial omics, single-cell omics, which provide an unparalleled opportunity for knowledge discovery in intractable diseases such as the discovery of biomarkers, functional modules, causal pathways, and regulatory networks, etc., which have great potential to bolster the therapeutic pipelines.

Traditional statistical methods have been successfully applied to incorporate multi-omic datasets such as genome-wide association studies (GWAS), molecular quantitative trait loci (QTL) analysis, and summarized Mendelian Randomization. However, limited pre-defined modalities have restricted the flexibility of available omics data, and the ability to incorporate different types of features of existing methods is still insufficient, which both decrease the power in knowledge discovery. Considering these aspects, advanced data mining, statistical, and machine learning methods are urgently needed to perform cross-modal data integration and modeling. This workshop aims to show the advanced data mining and statistical approaches which are helpful to discover disease-related knowledge and illuminate molecular mechanisms of complex diseases. Topics of interest include but are not limited to:
• Statistical methods and applications for integrating multimodal omics data.
• Machine learning methods of feature representation for multimodal omics.
• Graph-based deep learning methods for disease-related node/linkage prediction.
• Identification of molecular biomarkers for complex diseases.
• Disease-related module identification and validation through integrating multimodal omics data.
• Database and web tools for depositing and visualizing disease-related knowledge discovered based on multimodal omics.

Keywords:
Bioinformatics, Multi-Omics, Knowledge Discovery, Disease