The advancement of biomedical engineering technology has enabled the generation of multi-omics data by developing high-throughput technologies, including next-generation sequencing, mass spectrometry, and microarray analysis. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in various research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables to obtain a more comprehensive understanding of complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into one learning model also comes with challenges. Machine learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. weiterlesen