Big Data in Systems Biology

In the era of big data, the field of systems biology is witnessing an unprecedented surge in the availability and diversity of biological and clinical data. The synthesis of multi-modal data, encompassing imaging, genetics, proteomics, metabolomics, phenomics, and more, has emerged as a vital research area. This integrated approach aims to unravel the complex interplay of biological mechanisms, accurately pinpoint risk factors, and refine disease onset and prognosis predictions. Although notable progress has been made with statistical and machine learning methodologies over the past decade, a predominant focus on single outcomes and single modality in many studies, such as genome-wide association studies (GWAS), limits insights into multifaceted disease mechanisms and patient heterogeneity. Current models often fall short, especially in underrepresented populations, underscoring the necessity for integrative analyses that encompass multiple outcomes and better capture the biological pathways critical for developing precision medicine strategies.
To advance precision medicine and ensure equitable insights across diverse populations, it is not only essential to integrate multi-modal data but also to address the practical and ethical challenges inherent in data sharing and collaboration. As biological and clinical data become increasingly distributed across institutions and geographic regions, it becomes crucial to develop analytical frameworks that uphold data privacy and account for the geographic distribution of biological data, thereby avoiding the challenges associated with transferring data across centres or research thrusts.
This Research Topic aims to encourage a collaborative discussion on overcoming key challenges in analyzing multiple outcomes and deriving robust inferences from diverse data sources like multi-omics, single-cell omics, and multi-modal imaging. By harnessing information from various perspectives within or across studies, integrative analyses are poised to deliver a more thorough understanding of disease mechanisms, paving the way for enhanced and tailored patient care.
To gather further insights into these complex phenomena, we welcome articles addressing, but not limited to, the following themes:
o Novel methods to depict inter-relationships and networking among multi-omics data
o Predictive models for disease onset using multi-modality data
o Techniques for integrating genomics, imaging, and phenomics data across studies
o Data fusion methodologies for single-cell and spatial omics research
o Strategies to manage study heterogeneity and batch effects in systems biology research
o Federated architectures for multi-modal integration, privacy-preserving machine learning algorithms, and methods for updating models with real-time biological data streams
We expect contributions to encompass original research, methodological advancements, reviews, and opinion articles. Authors are encouraged to share their code, data, and algorithms openly, and to validate model predictions against experimental data, including published resources.