Single View and Multiview Signed Graph Learning - Applications to Gene Regulatory Network Inference
Explore signed graph learning techniques and their applications in gene regulatory network inference in this IEEE Signal Processing Society webinar. Delve into single-view and multi-view approaches, focusing on smoothness-based graph learning and its implementation in single-cell RNA sequencing data analysis. Examine the framework, sensitivity, and time complexity of these methods, and learn about data curation processes. Investigate multi-view signed graph learning, its applications, and hyperparameter selection. Compare results across different approaches and discuss potential extensions. Gain insights into computational issues and participate in a Q&A session to deepen your understanding of this cutting-edge topic in data science and graph theory.