Chapter 7 - Analytics methods and tools for integration of biomedical data in medicine

Abstract

Recent technologies have enabled us to collect diverse types of genome-wide data at an unprecedented scale and in multiple dimensions. Integrative computational methods are greatly needed to combine these data to provide a comprehensive view of the underlying biology and human disease. An ideal method should be able to automatically extract relevant features by exploiting heterogeneous data across various modalities and dimensions to answer a specific biological or medical question. The key challenge in developing such methods is the design of a comprehensive model capable of harnessing noisy and high-dimensional datasets without much manual supervision. Recent advances in machine learning (ML) algorithms, especially deep learning, offer a unique opportunity to mine and provide a systematic understanding of massive heterogeneous biological datasets. In this chapter, we describe the principles of integrative genomic analysis and discuss existing ML methods. We provide examples of successful data integration in biology and medicine with a specific focus on omics (e.g., single-cell RNA-seq) and image data. Finally, we discuss current challenges in integrative methods for genomics and our perspective on the future development of the field.