Understanding Molecular Complexity for Precision Medicine

on YouTube ↗33m

" summary

Graph topology methods convert heterogeneous omics into an iCell integrated network using graph-regularized NMTF and graphlet counts; the top 500 most rewired genes in iCells are significantly enriched for cancer drivers (validated literature and knockdowns across 63 candidates) while only 17 of those 63 (27%) are differentially expressed, showing expression alone misses drivers. Pan-cancer runs (20 TCGA types) replicate enrichment. Early COVID repurposing flags ~150 candidate drugs with ~30% already in trials. Methods run on supercomputers and are commercialized via a startup.

AI-written summary · about

" chapters (6)

1. 0:00Start
2. 1:08Molecular Networks
3. 2:41Types of Omix Data
4. 13:29Network Motifs
5. 18:21Molecular Interaction Networks
6. 25:54Network Embeddings

" related talks

• 32mMulti-Omic Integration for Understanding DiseaseRon Shamir · Computational Challenges in Very Large-Scale 'Omics'
• 33mNetwork Data Integration Enables Precision MedicineNatasa Przulj · Network Biology 2016
• 44mNetwork Propagation as a Tool for Deciphering Disease MechanismsRoded Sharan · Network Biology 2016
• 30mLeveraging Molecular Data for Drug DiscoveryHaiyan Huang · Computational Challenges in Very Large-Scale 'Omics'
• 29mMachine Learning Techniques for Omics Data AnalysisVesna Pajic · DSC Europe 23
• 53mThe Algorithm for Precision MedicineMatthew Might · DeclMed 2023
• 39mIdentifying Disease Mechanisms Using Gene Networks as PriorsAnna Goldberg · Network Biology 2016
• 50mThe algorithm for precision medicineMatt Might · Crunch Conference 2019