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| Main Authors: | , , , , |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.18329558 |
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Table of Contents:
- <p><span lang="EN-US">Comprehensive biological research shows that genomic information garnered over the years is not enough to completely understand biological systems even at the cellular level.<span> Integrative </span>omics focuses on the <span>integration of multiple omics data types, with </span>an unceasing improvement of high-content, real-time, multimodal, multi-omics technologies. This will lead to a deep understanding of biological systems. Multi-omics can be used to profile genetic, transcriptomic, epigenetic, spatial, proteomic and lineage information in single cells. This transformative method provides bioinformatics and integrative methods that can be used through multiple types of data, and it can identify relationships within cellular modalities, provide a deeper representation of cell state, and aid assembly of data sets to provide useful knowledge. Here, we discuss the challenges of multiple omics datatype integration, limitations of the complex machine learning models and recent technology advances in multi-omics data integration.</span></p>