Multiomics Data for Cancer Diagnosis (AlphaCare: Episode 3)

The amount of molecular biology datasets available are growing exponentially every month. Multiomics consist of all the layers of the molecular biome; the genome, epigenome, transcriptome, proteome, and metabolome. In this episode, we're going to learn how each of the layers of the molecular biology stack work, and then look at 3 different real world use cases for Cancer patients (diagnostic, prognostic, and predictive) using open-source python code on GitHub. Then we'll look at how a Generative Adversarial Network can be used to generate synthetic genomic data to battle imbalanced classes. Enjoy! Big thanks to RapidAPI for sponsoring this video series: https://rapidapi.com/hub?utm_source=Siraj-Raval&utm_medium=DevRel&utm_campaign=DevRel Omics Datasets: https://www.omicsdi.org/search More Omics Datasets: https://www.ncbi.nlm.nih.gov/geo/browse/?view=series&display=20&zsort=samples Code Credits: Example 1 (CITE-Seq autoencoder prognosis): https://github.com/naity/citeseq_autoencoder Example 2 (UMAP diagnosis): https://github.com/NikolayOskolkov/UMAPDataIntegration Example 3 (AlphaFold Protein Folding Prediction: https://alphafold.ebi.ac.uk/ https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb#scrollTo=KK7X9T44pWb7 Example 4 (GAN for Genomic Data Synthesis): https://github.com/Unique-Divine/GANs-for-Genomics Generative Biome HTML: https://github.com/marpi/biomes More Learning Resources: AlphaCare Episode 1: https://www.youtube.com/watch?v=fGv6VmfGMLc AlphaCare Episode 2: https://www.youtube.com/watch?v=PrqdEJZOrWE&t=10s AlphaFold Tutorial: https://www.youtube.com/watch?v=GQH-zWUylPY How I Gamify Learning: https://www.youtube.com/watch?v=nH85q7gC8uw Learn Machine Learning in 3 months: https://www.youtube.com/watch?v=Cr6VqTRO1v0 Multiomics Paper 1: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003173/ Multiomics Paper 2: https://www.frontiersin.org/articles/10.3389/fgene.2020.610798/full Follow me: INSTAGRA