Results 81 to 90 of about 74,563 (305)
Diversity and complexity in neural organoids
FEBS Letters, EarlyView.Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
CoRRCoresets have become an invaluable tool for solving $k$-means and kernel $k$-means clustering problems on large datasets with small numbers of clusters. On the other hand, spectral clustering works well on sparse graphs and has recently been extended to scale efficiently to large numbers of clusters.
Ben Jourdan +3 more
openaire +3 more sources
Compressive Spectral Clustering
CoRR, 2016 Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes.
Tremblay, Nicolas +3 more
openaire +4 more sources
Mini-batch spectral clustering [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets. While approximations recover computational tractability, they can potentially affect clustering performance. This paper proposes a practical approach to learn spectral clustering based on adaptive stochastic gradient optimization.
Yufei Han, Maurizio Filippone
openaire +2 more sources
A Short Text Clustering Algorithm Based on Spectral Cut [PDF]
Jisuanji gongcheng, 2016 Short text has the characteristics of sparsity and high dimension,and the existing clustering algorithm for the large-scale short text has low accuracy and efficiency.Aiming at this problem,a novel clustering method based on spectral clustering theory ...
LI Xiaohong,XIE Meng,MA Huifang,HE Tingnian
doaj +1 more source
, 2007 Wittkop T, Baumbach J, Lobo FP, Rahmann S. Large scale clustering of protein sequences with FORCE - a layout based heuristic for weighted cluster editing. BMC Bioinformatics.
Wittkop, Tobias +11 more
core +1 more source
Modulation of Homer1 EVH1 domain internal dynamics by putative autism‐associated mutations
FEBS Letters, EarlyView.The putative autism‐associated M65I and S97L variants of the EVH1 domain of the postsynaptic scaffold protein Homer1 do not exhibit substantial changes in their overall structure or partner binding. Both of them, but especially the M65I variant, show altered internal dynamics relative to the wild‐type domain on the μs‐ms timescale, indicated by the ...
Fanni Farkas +6 more
wiley +1 more source
Kernel Spectral Clustering and Applications [PDF]
, 2016 In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective.
Langone, Rocco +3 more
openaire +2 more sources
Remote Sensing, 2016 Remote sensing image clustering is a challenging task considering its intrinsic complexity. Recently, by combining the spectral and spatial information of the remote sensing data, the clustering performance can be dramatically enhanced, termed as ...
Ailong Ma, Yanfei Zhong, Liangpei Zhang
doaj +1 more source
Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana [PDF]
, 2009 Motivation: Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose.
Buchanan-Wollaston, Vicky +11 more
core +1 more source