Damiance, Jr., A.P.G.[Antonio P.G.],
Zhao, L.[Liang],
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0410
Gene expression analysis.
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Elsevier DOI Hierarchical clustering; Graphics processing units (GPUs); Gene
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Mitra, S.[Sushmita],
Banka, H.[Haider],
Multi-Objective Evolutionary Biclustering of Gene Expression Data,
PR(39), No. 12, December 2006, pp. 2464-2477.
Elsevier DOI
0609
Multi-objective optimization; Microarray; Genetic algorithms;
Knowledge discovery
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Lee, J.G.[Jian-Guo],
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0609
Gene expression; Metric learning; Manifold learning; Nearest neighbor
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Lin, T.C.[Tsun-Chen],
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Elsevier DOI
0609
Gene expression profiling; Cancer classification;
Genetic algorithm; Silhouette statistics
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Dynamic agglomerative clustering of gene expression profiles,
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Elsevier DOI
0704
Dynamic agglomerative clustering; Gene expression profile;
Mini-cluster gene; Scattered gene; Silhouette width; Singleton gene
BibRef
Kaban, A.[Ata],
On Bayesian classification with Laplace priors,
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Elsevier DOI
0706
Laplace prior; Variational Bayes; Sparsity; Shrinkage effect;
Predictive features; Microarray gene expressions
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Cao, K.[Kajia],
Zhu, Q.M.[Qiu-Ming],
Iqbal, J.[Javeed],
Chan, J.W.C.[John W.C.],
A trend pattern assessment approach to microarray gene expression
profiling data analysis,
PRL(28), No. 12, 1 September 2007, pp. 1472-1482.
Elsevier DOI
0707
Gene expression profiling; Microarray data analysis; Boundary points;
Dynamical patterns; Trend evaluations; Fisher's discriminate criterion
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Wang, H.Q.[Hong-Qiang],
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Huang, D.S.[De-Shuang],
Shu, J.[Jun],
Extracting gene regulation information for cancer classification,
PR(40), No. 12, December 2007, pp. 3379-3392.
Elsevier DOI
0709
Cancer classification; Gene expression levels; Gene regulation;
Microarray; Prediction strength
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Gilissen, C.[Christian],
Egmont-Petersen, M.[Michael],
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Elsevier DOI
0711
Statistics; Scale-space theory; Bivariate Gaussian integration; Gene expression
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A modified correlation coefficient based similarity measure for
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PRL(29), No. 3, 1 February 2008, pp. 232-242.
Elsevier DOI
0801
Pearson's correlation coefficient; Spearmann's correlation coefficient;
Modified correlation coefficient; Similarity; Clustering;
Time-course gene expression data
BibRef
Mancuso, K.[Katherine],
Hendrickson, A.E.[Anita E.],
Connor, Jr., T.B.[Thomas B.],
Mauck, M.C.[Matthew C.],
Kinsella, J.J.[James J.],
Hauswirth, W.W.[William W.],
Neitz, J.[Jay],
Neitz, M.[Maureen],
Recombinant adeno-associated virus targets passenger gene expression to
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JOSA-A(24), No. 5, May 2007, pp. 1411-1416.
WWW Link.
0801
BibRef
Fraser, K.[Karl],
Wang, Z.D.[Zi-Dong],
Li, Y.M.[Yong-Min],
Kellam, P.[Paul],
Liu, X.H.[Xiao-Hui],
Can graph-cutting improve microarray gene expression reconstructions?,
PRL(29), No. 16, 1 December 2008, pp. 2129-2136.
Elsevier DOI
0811
cDNA; Two channel; Microarrays; Background reconstruction; Graph-edge cuts
BibRef
Liew, A.W.C.[Alan Wee-Chung],
Law, N.F.[Ngai-Fong],
Cao, X.Q.[Xiao-Qin],
Yan, H.[Hong],
Statistical power of Fisher test for the detection of short periodic
gene expression profiles,
PR(42), No. 4, April 2009, pp. 549-556.
Elsevier DOI
0812
Gene expression profiles; Periodicity detection; Fisher exact test;
g-Statistic; Short signal
BibRef
Geng, B.[Bo],
Zhou, X.B.[Xiao-Bo],
Hung, Y.S.,
Growing enzyme gene networks by integration of gene expression, motif
sequence, and metabolic information,
PR(42), No. 4, April 2009, pp. 557-561.
Elsevier DOI
0812
Gene networks; Gene expression; Motif sequence; Metabolic reaction
BibRef
Hong, J.H.[Jin-Hyuk],
Cho, S.B.[Sung-Bae],
Gene boosting for cancer classification based on gene expression
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PR(42), No. 9, September 2009, pp. 1761-1767.
Elsevier DOI
0905
Gene selection; Cancer classification; Wrapper method; Filter method; Boosting
BibRef
Zhao, H.Y.[Hong-Ya],
Chan, K.L.[Kwok Leung],
Cheng, L.M.[Lee-Ming],
Yan, H.[Hong],
A probabilistic relaxation labeling framework for reducing the noise
effect in geometric biclustering of gene expression data,
PR(42), No. 11, November 2009, pp. 2578-2588.
Elsevier DOI
0907
Gene expression data analysis; Clustering; Biclustering; Hough
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Mukhopadhyay, A.[Anirban],
Maulik, U.[Ujjwal],
Towards improving fuzzy clustering using support vector machine:
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PR(42), No. 11, November 2009, pp. 2744-2763.
Elsevier DOI
0907
Microarray gene expression data; Fuzzy clustering; Cluster validity
indices; Variable string length genetic algorithm; Support vector
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BibRef
Gupta, N.[Neelima],
Aggarwal, S.[Seema],
MIB: Using mutual information for biclustering gene expression data,
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Elsevier DOI
1006
Biclustering; Gene expression data; Mutual information; GO term and
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Pittelkow, Y.E.[Yvonne E.],
Wilson, S.R.[Susan R.],
A novel statistical model for finding patterns in cell-cycle
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Elsevier DOI
1003
h-Profile plots; Covariance biplots; Time course experiment; Periodic
genes; Absolute sine model; Gene expression microarrays
BibRef
Cheng, K.O.,
Law, N.F.,
Siu, W.C.,
Iterative bicluster-based least square framework for estimation of
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PR(45), No. 4, April 2012, pp. 1281-1289.
Elsevier DOI
1112
Missing value imputation; Biclustering; Iterative estimation; Gene
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BibRef
Yu, Z.W.[Zhi-Wen],
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Han, G.Q.[Guo-Qiang],
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1201
Cluster ensemble; Data transformation; Data mining; Gene expression profile
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Clustering of temporal gene expression data by regularized spline
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1003
Spline model; Regularized regression; Energy operator; Temporal gene
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Wang, J.J.[Jia-Jun],
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Elsevier DOI
1301
Biclustering; Microarray; Gene expression data; Missing data
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Roy, S.[Swarup],
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Kalita, J.K.[Jugal K.],
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1308
Biclustering
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Derfoul, R.,
Le Guyader, C.,
A Relaxed Problem of Registration Based on the Saint Venant-Kirchhoff
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1412
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Wang, Y.,
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1905
Cancer, Mutual information, Gene expression,
Biological information theory, Tumors, Biological processes,
joint mutual information
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Bian, C.[Chuang],
Wong, K.C.[Ka-Chun],
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Multiobjective Deep Clustering and its Applications in Single-cell
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2208
Clustering algorithms, Gene expression, Optimization, RNA,
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DOI Link
2212
Brownian motion, clustering, deep learning, extraction,
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2311
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Exemplar Guided Deep Neural Network for Spatial Transcriptomics
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WACV23(5028-5037)
IEEE DOI
2302
Award, WACV, Applications. Spatial transcriptomics, Gene expression prediction,
Deep learning, Graph convolution, Tissue slide image.
Neural networks, Benchmark testing, Transformers,
Throughput, Gene expression, Applications: Biomedical/healthcare/medicine
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2402
cuckoo search (CS), deep learning (DL),
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SEPAL: Spatial Gene Expression Prediction from Local Graphs,
CVAMD23(2286-2295)
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Recursive Convolutional Neural Networks for Epigenomics,
ICPR21(2567-2574)
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2105
Deep learning, Genomics, Tools, Data models,
Convolutional neural networks, Gene expression
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Genexplorer: Visualizing and Comparing Gene Expression Levels via
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Decoding Gene Expression in 2D and 3D,
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1706
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Automatic generation of biclusters from gene expression data using
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ICPR16(2174-2179)
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1705
Clustering algorithms, Computer science,
Gene expression, Linear programming, Simulated annealing, AMOSA,
Biclustering, Gene expression data, Microarray technology,
Multi-objective, optimization
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Binary Pattern Dictionary Learning for Gene Expression Representation
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Information Bottleneck for Pathway-Centric Gene Expression Analysis,
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Observing change in crowded data sets in 3D space:
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3DUI13(173-174)
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1406
biology computing
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BiCluster Viewer: A Visualization Tool for Analyzing Gene Expression
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1109
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Landmark/image-based deformable registration of gene expression data,
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1106
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AP-Based Consensus Clustering for Gene Expression Time Series,
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1008
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0909
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Impact of Feature Selection on Support Vector Machine Using Microarray
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ICMV09(189-193).
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0912
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Semi-supervised method for gene expression data classification with
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ICPR08(1-4).
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Kashef, R.[Rasha],
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Efficient Bisecting k -Medoids and Its Application in Gene Expression
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0806
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Okun, O.[Oleg],
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Random Forest for Gene Expression Based Cancer Classification:
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Finding Rule Groups to Classify High Dimensional Gene Expression
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Exploiting the Geometry of Gene Expression Patterns for Unsupervised
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ICPR06(II: 670-674).
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The Wavelet Transformation for Temporal Gene Expression Analysis,
BioInfo05(III: 148-148).
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0507
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Joint Nonparametric Alignment for Analyzing Spatial Gene Expression
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Order-preserving clustering and its application to gene expression data,
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Feature selection and gene clustering from gene expression data,
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Extraction and Analysis of Cells .