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--></style></div><div data-marker="__QUOTED_TEXT__"><div class="WordSection1"><p class="MsoNormal">Bo Wang, Stanford University </p><p class="MsoNormal">Friday, April 27, 2018 - 12:30pm</p><p class="MsoNormal">Computer Science - Room 105</p><p class="MsoNormal">Host: Mona Singh</p><p class="MsoNormal"> </p><p class="MsoNormal">"Machine Learning Algorithms for Exploiting Spectral Structures of Biological Networks"</p><p class="MsoNormal"> </p><p class="MsoNormal">Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from populations to a single cell.<span style="mso-spacerun:yes"> </span>How to extract and understand non-trivial topological features and structures inherent in the networks is critical to understanding interactions within complicated biological systems. In this talk, I will introduce recent developments of machine learning algorithms that exploit spectral structures of networks for a wide range of biological applications, ranging from single-cell analysis to function prediction on protein-protein interaction networks. Specifically, I will first present a new method named SIMLR, combining both low-rank spectral regularization and multiple-kernel learning, to construct cell networks for sparse noisy single-cell RNA-<span class="SpellE">seq</span> data. The learned cell networks will enable effective dimension reduction, clustering and visualization. Second, I will discuss a novel method, Network Enhancement (NE), that aims to de-noise complex networks such as protein-protein interaction networks without corrupting spectral structures of the networks, therefore improving function prediction accuracy. Last, I will also briefly introduce recent advances where deep convolutional neural network is applied on biological networks (e.g., drug-target network) as a first-order spectral approximation of network structures.</p><p class="MsoNormal"> </p><p class="MsoNormal">Bio: </p><p class="MsoNormal">Bo Wang is a recent PhD graduate in Computer Science at Stanford University, an IEEE and CVF Fellow and an NSF Graduate Research Fellow.<span style="mso-spacerun:yes"> </span>His research focuses on machine learning (particularly deep learning) on many applications in computer vision (e.g., image segmentation, object detection) and computational biology (e.g., single-cell analysis, integrative cancer subtyping). Prior to Stanford, he received his master degree at University of Toronto, majoring in numerical analysis.</p></div><br></div></div></body></html>