TL;DR
This chapter presents a data-driven bioinformatic framework for biomarker discovery based on single-omics and multi-omics datasets, with a particular focus on allergy-related applications and describes complementary strategies for biomarker prioritization.
Introduction Allergic and inflammatory respiratory conditions are complex and heterogeneous diseases affecting a large portion of the population. Although omics technologies have enabled the identification of potential biomarkers, single-omics-based approaches often fail to capture the full complexity of these disorder...
The rapid accumulation of multi-omics data provides increasing opportunities for cancer biomarker discovery, but the heterogeneity and high dimensionality of these datasets remain major challenges for data integration and analysis. This review examines the application of bioinformatics and artificial intelligence for m...
Multi-omics studies provide opportunities to investigate complex biological systems, but their interpretation can be complicated by numerous sources of variation. Here, we discuss the biological, analytical, and computational factors that can affect the reproducibility of omics studies. We introduce practical recommend...
Multi-omics is a field that studies the interactions between different biological modalities to obtain a more precise medical understanding of biological phenomena. With recent advances in high-throughput technologies, multi-omics datasets have become more accessible, enabling a better characterization of clinical obse...
The synergistic relationship between Artificial Intelligence and Medicine has the potential to revolutionize our understanding of how genomics, transcriptomics, and other omics influence susceptibility to specific diseases. Finding the most appropriate biomarkers from omics is still a challenge for most diseases, b...
Integrating heterogeneous multi-omics data with machine learning (ML) provides a powerful framework for interpreting genomic variants and advancing pharmacogenomic research in precision medicine. High-throughput technologies have generated vast datasets spanning genomics, transcriptomics, epigenomics, and proteomics, y...
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