F
Fuhui Long
Researcher at Allen Institute for Brain Science
Publications - 51
Citations - 13700
Fuhui Long is an academic researcher from Allen Institute for Brain Science. The author has contributed to research in topics: Segmentation & Automatic image annotation. The author has an hindex of 28, co-authored 50 publications receiving 11769 citations. Previous affiliations of Fuhui Long include Lawrence Berkeley National Laboratory & Stanford University.
Papers
More filters
Journal ArticleDOI
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Journal ArticleDOI
A GAL4-Driver Line Resource for Drosophila Neurobiology
Arnim Jenett,Gerald M. Rubin,Teri-T B. Ngo,David Shepherd,Christine Murphy,Heather Dionne,Barret D. Pfeiffer,Amanda Cavallaro,Donald Hall,Jennifer Jeter,Nirmala Iyer,Dona Fetter,Joanna H. Hausenfluck,Hanchuan Peng,Eric T. Trautman,Robert Svirskas,Eugene W. Myers,Zbigniew R. Iwinski,Yoshinori Aso,Gina M. DePasquale,Adrianne Enos,Phuson Hulamm,S. Lam,Hsing-Hsi Li,Todd R. Laverty,Fuhui Long,Lei Qu,Sean D. Murphy,Konrad Rokicki,Todd Safford,Kshiti Shaw,Julie H. Simpson,Allison Sowell,Susana Tae,Yang Yu,Christopher T. Zugates +35 more
TL;DR: The utility of 7,000 transgenic lines of Drosophila melanogaster for identifying novel neuronal cell types, revealing brain asymmetry, and describing the nature and extent of neuronal shape stereotypy is illustrated.
Journal ArticleDOI
V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets
TL;DR: V3D-Neuron can precisely digitize the morphology of a single neuron in a fruitfly brain in minutes, with about a 17-fold improvement in reliability and tenfold savings in time compared with other neuron reconstruction tools.
Book ChapterDOI
Fundamentals of Content-Based Image Retrieval
TL;DR: This chapter introduces in this chapter some fundamental theories for content-based image retrieval, and introduces in detail some widely used methods for visual content descriptions.
Journal ArticleDOI
An anatomic transcriptional atlas of human glioblastoma
Ralph B. Puchalski,Nameeta Shah,Jeremy A. Miller,Rachel A. Dalley,Steve R. Nomura,Jae Guen Yoon,Kimberly A. Smith,Michael Lankerovich,Darren Bertagnolli,Kris Bickley,Andrew F. Boe,Krissy Brouner,Stephanie Butler,Shiella Caldejon,Mike Chapin,Suvro Datta,Nick Dee,Tsega Desta,Tim A. Dolbeare,Nadezhda Dotson,Amanda Ebbert,David Feng,Xu Feng,Michael S. Fisher,Garrett Gee,Jeff Goldy,Lindsey Gourley,Benjamin W. Gregor,Guangyu Gu,Nika Hejazinia,John G. Hohmann,Parvinder Hothi,Robert Howard,Kevin M. Joines,Ali Kriedberg,Leonard Kuan,Chris Lau,Felix Lee,Hwahyung Lee,Tracy Lemon,Fuhui Long,Naveed Mastan,Erika Mott,Chantal Murthy,Kiet Ngo,Eric Olson,Melissa Reding,Zack Riley,David Rosen,David Sandman,Nadiya V. Shapovalova,Clifford R. Slaughterbeck,Andrew Sodt,Graham Stockdale,Aaron Szafer,Wayne Wakeman,Paul Wohnoutka,Steven J. White,Don Marsh,Robert C. Rostomily,Robert C. Rostomily,Lydia Ng,Chinh Dang,Allan R. Jones,Bart Keogh,Haley Gittleman,Jill S. Barnholtz-Sloan,Patrick J. Cimino,Megha S Uppin,C. Dirk Keene,Farrokh Farrokhi,Justin D. Lathia,Michael E. Berens,Antonio Iavarone,Amy Bernard,Ed S. Lein,John W. Phillips,Steven Rostad,Charles Cobbs,Michael Hawrylycz,Greg Foltz +80 more
TL;DR: The Ivy Glioblastoma Atlas is presented, an anatomically based transcriptional atlas of human gliOBlastoma that aligns individual histologic features with genomic alterations and gene expression patterns, thus assigning molecular information to the most important morphologic hallmarks of the tumor.