Title: Understanding Complex Systems using the Functional Connectome
Speaker: Leighton T. Izu
Department of Pharmacology
Ca^{2+}rdiac Signaling Lab
University of California, Davis
Abstract: A fundamental challenge in biology is determining how parts of a complex system (e.g., a muscle cell or a human) need to be coordinated to produce a given output. We recently developed a method called the Functional Connectome (FC) that determines this coordination pattern. The FC exploits the natural substructures inherent in the data that are found from the singular value decomposition of the data matrix. I’ll illustrate how the FC is found and what we can learn from it using a wine data set and data on cardiac muscle cells from our lab.
Please email: spost ‘at’ hawaii.edu for zoom link
Location: Zoom Meeting https://hawaii.zoom.us/j/95463949444 Meeting ID: 954 6394 9444 Passcode: mathama |
Topological data analysis (TDA) is a new approach to analyzing complexdata which often helps reveal otherwise hidden patterns by highlightingvarious geometrical and topological features of the data. Persistenthomology is a key in the TDA toolbox. It measures topological featuresof data that persist across multiple scales and thus are robust withrespect to noise. Persistent homology has had many successfulapplications, but there is room for improvement. For large datasets,computation of persistent homology often takes a significant amount oftime. Several approaches have been proposed to try to remedy this issue,such as witness complexes, but those approaches present their owndifficulties.