  With advanced imaging, sequencing, and profiling technologies, multiple omics
data become increasingly available and hold promises for many healthcare
applications such as cancer diagnosis and treatment. Multimodal learning for
integrative multi-omics analysis can help researchers and practitioners gain
deep insights into human diseases and improve clinical decisions. However,
several challenges are hindering the development in this area, including the
availability of easily accessible open-source tools. This survey aims to
provide an up-to-date overview of the data challenges, fusion approaches,
datasets, and software tools from several new perspectives. We identify and
investigate various omics data challenges that can help us understand the field
better. We categorize fusion approaches comprehensively to cover existing
methods in this area. We collect existing open-source tools to facilitate their
broader utilization and development. We explore a broad range of omics data
modalities and a list of accessible datasets. Finally, we summarize future
directions that can potentially address existing gaps and answer the pressing
need to advance multimodal learning for multi-omics data analysis.
