scholarly journals Total Variation Constrained Graph Manifold Learning Strategy for Cerenkov Luminescence Tomography

2021 ◽  
Author(s):  
Guo hongbo ◽  
Jingjing Yu ◽  
Xuelei He ◽  
Huangjian Yi ◽  
Yuqing Hou ◽  
...  
Keyword(s):  
Total Variation ◽  
Manifold Learning ◽  
Learning Strategy ◽  
Graph Manifold

scholarly journals GEM: A manifold learning based framework for reconstructing spatial organizations of chromosomes

2017 ◽  
Author(s):  
Guangxiang Zhu ◽  
Wenxuan Deng ◽  
Hailin Hu ◽  
Rui Ma ◽  
Sai Zhang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Learning Strategy ◽  
Genome Structure ◽  
Three Dimensional ◽  
Conformational Energy ◽  
3D Genome ◽  
Learning Framework ◽  
Reconstruction Methods ◽  
Eukaryotic Gene ◽  
Validation Tests

AbstractDecoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, Chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to elucidate the underlying 3D spatial organizations of chromosomes from Hi-C data. Unlike previous chromatin structure reconstruction methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances between distal genomic loci, GEM is able to reconstruct an ensemble of chromatin conformations by directly embedding the neigh-boring affinities from Hi-C space into 3D Euclidean space based on a manifold learning strategy that considers both the fitness of Hi-C data and the biophysical feasibility of the modeled structures, which are measured by the conformational energy derived from our current biophysical knowledge about the 3D polymer model. Extensive validation tests on both simulated interaction frequency data and experimental Hi-C data of yeast and human demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also reconstructed accurate chromatin structures that agreed well with the hold-out or independent Hi-C data and sparse geometric restraints derived from the previous fluorescence in situ hybridization (FISH) studies. In addition, as GEM can generate accurate spatial organizations of chromosomes by integrating both experimentally-derived spatial contacts and conformational energy, we for the first time extended our modeling method to recover long-range genomic interactions that are missing from the original Hi-C data. All these results indicated that GEM can provide a physically and physiologically valid 3D representations of the organizations of chromosomes and thus serve as an effective and useful genome structure reconstructor.

scholarly journals Inside Cover: Sparse‐graph manifold learning method for bioluminescence tomography (J. Biophotonics 4/2020)

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Hongbo Guo ◽  
Ling Gao ◽  
Jingjing Yu ◽  
Xiaowei He ◽  
Hai Wang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Sparse Graph ◽  
Learning Method ◽  
Bioluminescence Tomography ◽  
Graph Manifold

scholarly journals Performance assessment of a recent change detection method for homogeneous and heterogeneous images

2015 ◽  
pp. 23-29
Author(s):  
Jorge Prendes ◽  
Marie Chabert ◽  
Frédéric Pascal ◽  
Alain Giros ◽  
Jean-Yves Tourneret
Keyword(s):  
Statistical Model ◽  
Change Detection ◽  
Manifold Learning ◽  
Expectation Maximization ◽  
Learning Strategy ◽  
Detection Method ◽  
Recent Change ◽  
Sar Images ◽  
Estimated Parameters ◽  
Optical Images

A statistical model for detecting changes in remote sensing images has recently been proposed in (Prendes et al., 2014a,b). This model is sufficiently general to be used for homogeneous images acquired by the same kind of sensors (e.g., two optical images from Pléiades satellites, possibly with different acquisition conditions), and for heterogeneous images acquired by different sensors (e.g., an optical image acquired from a Pléiades satellite and a synthetic aperture radar (SAR) image acquired from a TerraSAR-X satellite). This model assumes that each pixel is distributed according to a mixture of distributions depending on the noise properties and on the sensor intensity responses to the actual scene. The parameters of the resulting statistical model can be estimated by using the classical expectation-maximization (EM) algorithm. The estimated parameters are finally used to learn the relationships between the images of interest, via a manifold learning strategy. These relationships are relevant for many image processing applications, particularly those requiring a similarity measure (e.g., image change detection and image registration). The main objective of this paper is to evaluate the performance of a change detection method based on this manifold learning strategy initially introduced in (Prendes et al., 2014a,b). This performance is evaluated by using results obtained with pairs of real optical images acquired from Pléiades satellites and pairs of optical and SAR images.

Sparse‐graph manifold learning method for bioluminescence tomography

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Hongbo Guo ◽  
Ling Gao ◽  
Jingjing Yu ◽  
Xiaowei He ◽  
Hai Wang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Sparse Graph ◽  
Learning Method ◽  
Bioluminescence Tomography ◽  
Graph Manifold

Is testing a more effective learning strategy than note-taking?

2017 ◽  
Vol 23 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Ralf Rummer ◽  
Judith Schweppe ◽  
Kathleen Gerst ◽  
Simon Wagner
Keyword(s):  
Learning Strategy ◽  
Note Taking ◽  
Effective Learning
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