Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways Mark L. Grimes1*, Wan-Jui Lee2, Laurens van der Maaten2, Paul Shannon3 1 Division of Biological Sciences, University of Montana, Missoula, Montana, U

First Page		Document Content
Date: 2016-07-16 15:30:43 Tyrosine kinase receptors Statistics Cluster analysis Data analysis Computational statistics Hierarchical clustering Network analysis Anaplastic lymphoma kinase K-means clustering Tyrosine kinase Dendrogram Epidermal growth factor receptor		Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways Mark L. Grimes1*, Wan-Jui Lee2, Laurens van der Maaten2, Paul Shannon3 1 Division of Biological Sciences, University of Montana, Missoula, Montana, U Add to Reading List Source URL: lvdmaaten.github.io Download Document from Source Website File Size: 1,77 MB Share Document on Facebook

	K-means Clustering Mohammad Emtiyaz Khan EPFL Nov 3, 2015 c DocID: 1uYYl - View Document
	2012 Second Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization & Transmission Boosting the computational performance of feature-based multiple 3D scan alignment by iat-k-means clustering Nicola DocID: 1ux3g - View Document
	Uniform Deviation Bounds for k-Means Clustering Olivier Bachem 1 Mario Lucic 1 S. Hamed Hassani 1 Andreas Krause 1 Abstract Uniform deviation bounds limit the difference between a model’s expected loss and its loss on DocID: 1tHo7 - View Document
	Uniform Deviation Bounds for k-Means Clustering Olivier Bachem 1 Mario Lucic 1 S. Hamed Hassani 1 Andreas Krause 1 Abstract Uniform deviation bounds limit the difference between a model’s expected loss and its loss on DocID: 1tDsP - View Document
	Clustering: K -means and Kernel K -means Piyush Rai Machine Learning (CS771A) Aug 31, 2016 Machine Learning (CS771A) DocID: 1tlmt - View Document