Abstract
Simple SummaryIn recent years both research areas of next-generation sequencing and artificial intelligence have grown remarkably. Their intersection simultaneously gave rise to a panacea of different algorithms and applications. This article delineates tailored machine learning and systems biology approaches and combinations thereof that tackle the various challenges that arise in the face of big data. Moreover, it provides an overview of the numerous applications of artificial intelligence aiding the analysis and interpretation of next-generation sequencing data.The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.
Highlights
The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine
Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models
The tailored combination with machine learning and systems biology broadens our knowledge from genetics to medicine, in cancer research
Summary
The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine It led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In systems biology, data-driven approaches are applied to identify vital signaling pathways [1] This pathway-centric analysis is crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. An increasing number of DNN-based approaches integrated multi-omics data and systems biology structures into the learned models. Such approaches aim to adopt the DNN model on prior biological and medical knowledge and thereby improve our understanding of diseases and the effect mechanisms of medication in a new way. We will discuss very recent DNN-based approaches in cancer researches
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