body { text-align: justify} Today’s guest blog post on multiOmics integration is written by Aditi Qamra and edited by Tommy.
If you want to do a guest posting in my blog which gets 30k views per month, feel free to contact me on LinkedIn.
Aditi is a senior data scientist working on biomarker discovery and early product development at Roche, using multimodal clinical and genomic data. She has a PhD and postdoc in epigenomics of solid tumors and enjoys upskilling herself in stats topics.
Multi-omics data analysis is a cutting-edge approach in biology that involves studying and integrating information from multiple biological “omics” sources. These omics sources include genomics (genes and their variations), transcriptomics (gene expression and RNA data), proteomics (proteins and their interactions), metabolomics (small molecules and metabolites), epigenomics (epigenetic modifications), and more. By analyzing data from various omics levels together, we can gain a more comprehensive and detailed understanding of biological systems and their complexities.