Journal ArticleDOI
Clustering by Passing Messages Between Data Points
Brendan J. Frey,Delbert Dueck +1 more
TLDR
A method called “affinity propagation,” which takes as input measures of similarity between pairs of data points, which found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.Abstract:
Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.read more
Citations
More filters
疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Book
Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Journal ArticleDOI
Clustering by fast search and find of density peaks
Alex Rodriguez,Alessandro Laio +1 more
TL;DR: A method in which the cluster centers are recognized as local density maxima that are far away from any points of higher density, and the algorithm depends only on the relative densities rather than their absolute values.
Journal ArticleDOI
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Amit Zeisel,Ana B. Muñoz-Manchado,Simone Codeluppi,Peter Lönnerberg,Gioele La Manno,Anna Juréus,Sueli Marques,Hermany Munguba,Liqun He,Christer Betsholtz,Christer Betsholtz,Charlotte Rolny,Gonçalo Castelo-Branco,Jens Hjerling-Leffler,Sten Linnarsson +14 more
TL;DR: Large-scale single-cell RNA sequencing is used to classify cells in the mouse somatosensory cortex and hippocampal CA1 region and found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex.
Journal ArticleDOI
Systematic identification of genomic markers of drug sensitivity in cancer cells
Mathew J. Garnett,Elena J. Edelman,Sonja J. Heidorn,Christopher Greenman,Christopher Greenman,Anahita Dastur,King Wai Lau,Patricia Greninger,I. Richard Thompson,Xi Luo,Jorge Soares,Qingsong Liu,Francesco Iorio,Francesco Iorio,Didier Surdez,Li Chen,Randy J. Milano,Graham R. Bignell,Ah Ting Tam,Helen Davies,Jesse A. Stevenson,Syd Barthorpe,Stephen R. Lutz,Fiona Kogera,Karl P. Lawrence,Anne McLaren-Douglas,Xeni Mitropoulos,Tatiana Mironenko,Helen Thi,Laura Richardson,Wenjun Zhou,F Jewitt,Tinghu Zhang,Patrick O’Brien,Jessica L. Boisvert,Stacey Price,Wooyoung Hur,Wanjuan Yang,Xianming Deng,Adam Butler,Hwan Geun Choi,Jae Won Chang,José Baselga,Ivan Stamenkovic,Jeffrey A. Engelman,Sreenath V. Sharma,Sreenath V. Sharma,Olivier Delattre,Julio Saez-Rodriguez,Nathanael S. Gray,Jeffrey Settleman,P. Andrew Futreal,Daniel A. Haber,Daniel A. Haber,Michael R. Stratton,Sridhar Ramaswamy,Ultan McDermott,Cyril H. Benes +57 more
TL;DR: It was found that mutated cancer genes were associated with cellular response to most currently available cancer drugs, and systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.
References
More filters
Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.