Tutorial: Multi Modality Helps in Solving Biomedical Problems: Theory and Applications

With the exploration of omics technologies, researchers are able to collect high-throughput biomedical data. The explosion of these new frontier omics technologies produces diverse genomic datasets such as microarray gene expression, miRNA expression, DNA sequence, 3D structures etc. These different representations (modality) of the biomedical data contain distinct, useful and complementary information of different samples. As a consequence, there is a growing interest in collecting �multi-modal� data for the same set of subjects and integrating this heterogeneous information to obtain more profound insights into the underlying biological system. The current tutorial will discuss in detail different problems of bioinformatics and the concepts of multimodality in bioinformatics. In recent years different machine learning and deep learning based approaches become popular in dealing with multimodal data. Drawing attention from the above facts, this tutorial is a roadmap of existing deep multi-modal architectures in solving different computational biology problems. This tutorial will be an advanced survey equally of interest to academic researchers and industry practitioners - very timely with so much vibrant research in the computational biology domain over the past 5 years. As IEEE WCCI is an prestigious conference for discussion of neural network frontiers, this tutorial is very much relevant for IEEE WCCI.