Tensor Factorization for Zero-Inflated Multi-Dimensional Genomics Data

Explore a comprehensive lecture on tensor methods for analyzing multi-dimensional genomics data, delivered by Assistant Professor Neriman Tokcan from the University of Massachusetts Boston at the Broad Institute. Delve into the evolving landscape of omics data analysis, where multi-dimensional approaches have become increasingly crucial due to simultaneous data acquisition from various sources. Learn about tensor methods as analytical tools for handling complex high-dimensional datasets, including fundamental concepts of tensor algebra and factorization techniques. Discover practical applications in genomics, such as identifying gene expression modules, integrating multiple omics data types, handling missing value imputation, and analyzing ligand-receptor interaction patterns. Gain insights into how tensor methods effectively address the challenges posed by multi-sample, multi-condition datasets and the integration of data across different molecular levels in modern genomics research.