PPT-Gaussian embedding for large-scale gene set analysis

Sheng Wang Emily R Flynn amp Russ B Altman Gene sets Come from many sources Boost the signaltonoise ratio and increase explanatory power Used in various downstream analyses disease signature identification. 1 Scale space parameters 2 22 Detector parameters 3 23 Descriptor parameters 3 24 Direct access to SIFT components Computer Vision and Image Processing (CVIP). Ifeoma. Nwogu. inwogu@buffalo.edu. Lecture 11 – Local Features. 1. Schedule. Last class . We started local features. Today. More on local features. Readings for today: . Greg Cox. Richard Shiffrin. Continuous response measures. The problem. What do we do if we do not know the functional form?. Rasmussen & Williams, . Gaussian Processes for Machine Learning. http://www.gaussianprocesses.org/. Mikhail . Belkin. Dept. of Computer Science and Engineering, . Dept. of Statistics . Ohio State . University / ISTA. Joint work with . Kaushik. . Sinha. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . Non-normed spaces. Alexandr. . Andoni. (MSR). Embedding / Sketching. Definition. : an embedding . is a map . f:M. . H. . of a metric . (M, . d. M. ). into a host metric . (H, . . H. ). such that for any . Alexandr. . Andoni. (MSR). Definition by example. Problem. : Compute the diameter of a set . S. , of size . n. , living in . d. -dimensional . ℓ. 1. d. Trivial solution: . O(d * n. 2. ) . time. Will see solution in . Jongmin Baek and David E. Jacobs. Stanford University. . Motivation. Input. Gaussian. Filter. Spatially. Varying. Gaussian. Filter. Accelerating Spatially Varying. . Gaussian Filters . Accelerating. The dimension of an infinitely “crinkly” line > 1.. It’s “embedding space” is 2 dimensions.. The same can be done with a 2D sheet: infinitely crinkled it has dimension > . 2. .. It’s “embedding space” is 3 dimensions.. Gaussian degradation occurs in a large number of situations.. . the point-spread-function of the human lens e.g. has a close to Gaussian shape (for a 3 mm pupil . . is about 2 minutes of arc); . Blake Shaw, Tony . Jebara. ICML 2009 (Best Student Paper nominee). Presented by Feng Chen. Outline. Motivation. Solution. Experiments. Conclusion. Motivation. Graphs exist everywhere: web link networks, social networks, molecules networks, . Lecture . 2: Applications. Steven J. Fletcher. Cooperative Institute for Research in the Atmosphere. Colorado State University. Overview of Lecture. Do we linearize the Bayesian problem or do we find the Bayesian Problem for the linear increment?. MELL: Effective Embedding Method for Multiplex Networks International workshop on Mining Attributed Networks Lyon, 23 April 2018 Ryuta Matsuno 1,2 , Tsuyoshi Murata 1 1 Tokyo Institute of Technology, Tokyo, Japan transcriptomic. ) . data analysis. Ståle. . Nygård. , Bioinformatics core facility, OUS/UiO. staaln@ifi.uio.no. Gene expression. Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product.. by: R. Yang. ,. . J. Shi. ,. . X. Xiao. ,. . Y. Yang. ,. . J. Liu. ,. . and . S. . Bhowmick. Basic data analytics is easy.. Stock. Profit. Revenue. Market share. Overvalued?. Buy?. TSLA. $721m. $31B.