21.4.3 Gene Expression, Genome

Chapter Contents (Back)
Gene Expression. Genome Analysis.

Damiance, Jr., A.P.G.[Antonio P.G.], Zhao, L.[Liang], Carvalho, A.C.P.L.F.[Andre C.P.L.F.],
A dynamical model with adaptive pixel moving for microarray images segmentation,
RealTimeImg(10), No. 4, August 2004, pp. 189-195.
Elsevier DOI 0410
Gene expression analysis. BibRef

Zhang, Q.[Qiong], Zhang, Y.S.[Ying-Sha],
Hierarchical clustering of gene expression profiles with graphics hardware acceleration,
PRL(27), No. 6, 15 April 2006, pp. 676-681.
Elsevier DOI Hierarchical clustering; Graphics processing units (GPUs); Gene expression profile; Microarray 0604
BibRef

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 BibRef

Lee, J.G.[Jian-Guo], Zhang, C.S.[Chang-Shui],
Classification of gene-expression data: The manifold-based metric learning way,
PR(39), No. 12, December 2006, pp. 2450-2463.
Elsevier DOI 0609
Gene expression; Metric learning; Manifold learning; Nearest neighbor BibRef

Lin, T.C.[Tsun-Chen], Liu, R.S.[Ru-Sheng], Chen, C.Y.[Chien-Yu], Chao, Y.T.[Ya-Ting], Chen, S.Y.[Shu-Yuan],
Pattern classification in DNA microarray data of multiple tumor types,
PR(39), No. 12, December 2006, pp. 2426-2438.
Elsevier DOI 0609
Gene expression profiling; Cancer classification; Genetic algorithm; Silhouette statistics BibRef

Liang, F.M.[Fa-Ming], Wang, N.[Naisyin],
Dynamic agglomerative clustering of gene expression profiles,
PRL(28), No. 9, 1 July 2007, pp. 1062-1076.
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,
PRL(28), No. 10, 15 July 2007, pp. 1271-1282.
Elsevier DOI 0706
Laplace prior; Variational Bayes; Sparsity; Shrinkage effect; Predictive features; Microarray gene expressions BibRef

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 BibRef

Wang, H.Q.[Hong-Qiang], Wong, H.S.[Hau-San], 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 BibRef

Groot, P.[Perry], Gilissen, C.[Christian], Egmont-Petersen, M.[Michael],
Error probabilities for local extrema in gene expression data,
PRL(28), No. 15, 1 November 2007, pp. 2133-2142.
Elsevier DOI 0711
Statistics; Scale-space theory; Bivariate Gaussian integration; Gene expression BibRef

Son, Y.S.[Young Sook], Baek, J.S.[Jang-Sun],
A modified correlation coefficient based similarity measure for clustering time-course gene expression data,
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 cones in primate retina,
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 profiles,
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 transform; Probabilistic relaxation labeling BibRef

Mukhopadhyay, A.[Anirban], Maulik, U.[Ujjwal],
Towards improving fuzzy clustering using support vector machine: Application to gene expression data,
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 machines; Gene ontology BibRef

Gupta, N.[Neelima], Aggarwal, S.[Seema],
MIB: Using mutual information for biclustering gene expression data,
PR(43), No. 8, August 2010, pp. 2692-2697.
Elsevier DOI 1006
Biclustering; Gene expression data; Mutual information; GO term and transcription factor binding site BibRef

Pittelkow, Y.E.[Yvonne E.], Wilson, S.R.[Susan R.],
A novel statistical model for finding patterns in cell-cycle transcription data,
PRL(31), No. 14, 15 October 2010, pp. 2126-2132.
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 missing values in microarray gene expression data,
PR(45), No. 4, April 2012, pp. 1281-1289.
Elsevier DOI 1112
Missing value imputation; Biclustering; Iterative estimation; Gene expression analysis BibRef

Yu, Z.W.[Zhi-Wen], Wong, H.S.[Hau-San], You, J.[Jane], Yu, G.X.[Guo-Xian], Han, G.Q.[Guo-Qiang],
Hybrid cluster ensemble framework based on the random combination of data transformation operators,
PR(45), No. 5, May 2012, pp. 1826-1837.
Elsevier DOI 1201
Cluster ensemble; Data transformation; Data mining; Gene expression profile BibRef

Zhang, W.F.[Wei-Feng], Liu, C.C.[Chao-Chun], Yan, H.[Hong],
Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure,
PR(43), No. 12, December 2010, pp. 3969-3976.
Elsevier DOI 1003
Spline model; Regularized regression; Energy operator; Temporal gene expression data analysis; Clustering BibRef

Yan, D.[Dechun], Wang, J.J.[Jia-Jun],
Biclustering of gene expression data based on related genes and conditions extraction,
PR(46), No. 4, April 2013, pp. 1170-1182.
Elsevier DOI 1301
Biclustering; Microarray; Gene expression data; Missing data estimation BibRef

Roy, S.[Swarup], Bhattacharyya, D.K.[Dhruba K.], Kalita, J.K.[Jugal K.],
CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data,
PRL(34), No. 14, 2013, pp. 1669-1678.
Elsevier DOI 1308
Biclustering BibRef

Derfoul, R., Le Guyader, C.,
A Relaxed Problem of Registration Based on the Saint Venant-Kirchhoff Material Stored Energy for the Mapping of Mouse Brain Gene Expression Data to a Neuroanatomical Mouse Atlas,
SIIMS(7), No. 4, 2014, pp. 2175-2195.
DOI Link 1412
BibRef

Liu, Z.[Zhe], Song, Y.Q.[Yu-Qing], Xie, C.H.[Cong-Hua], Tang, Z.[Zheng],
A new clustering method of gene expression data based on multivariate Gaussian mixture models,
SIViP(10), No. 1, February 2016, pp. 359-368.
WWW Link. 1601
BibRef

Rosati, P.[Paolo], Lupascu, C.A.[Carmen A.], Tegolo, D.[Domenico],
Analysis of low-correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas,
IET-CV(12), No. 7, October 2018, pp. 996-1006.
DOI Link 1809
BibRef

Wang, Y., Li, X., Ruiz, R.,
Weighted General Group Lasso for Gene Selection in Cancer Classification,
Cyber(49), No. 8, August 2019, pp. 2860-2873.
IEEE DOI 1905
Cancer, Mutual information, Gene expression, Biological information theory, Tumors, Biological processes, joint mutual information BibRef

Wang, Y.H.[Yun-He], Bian, C.[Chuang], Wong, K.C.[Ka-Chun], Li, X.T.[Xiang-Tao], Yang, S.X.[Sheng-Xiang],
Multiobjective Deep Clustering and its Applications in Single-cell RNA-seq Data,
SMCS(52), No. 8, August 2022, pp. 5016-5027.
IEEE DOI 2208
Clustering algorithms, Gene expression, Optimization, RNA, Sequential analysis, Feature extraction, Sociology, single-cell RNA-seq dataset BibRef

Pandit, D.[Dhiren], Dhodiya, J.[Jayesh], Patel, Y.[Yogeshwari],
Molecular cancer classification on microarrays gene expression data using wavelet-based deep convolutional neural network,
IJIST(32), No. 6, 2022, pp. 2262-2280.
DOI Link 2212
Brownian motion, clustering, deep learning, extraction, Kalman filter, preprocessing, selection BibRef

Yang, Y.[Yan], Hossain, M.Z.[Md Zakir], Stone, E.A.[Eric A.], Rahman, S.[Shafin],
Spatial transcriptomics analysis of gene expression prediction using exemplar guided graph neural network,
PR(145), 2024, pp. 109966.
Elsevier DOI Code:
WWW Link. 2311
BibRef
Earlier:
Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction,
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 BibRef

Joshi, A.A.[Amol Avinash], Aziz, R.M.[Rabia Musheer],
Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data,
IJIST(34), No. 2, 2024, pp. e23007.
DOI Link 2402
cuckoo search (CS), deep learning (DL), minimum redundancy maximum relevance (mRMR), particle swarm optimization (PSO) BibRef


Mejia, G.[Gabriel], Cárdenas, P.[Paula], Ruiz, D.[Daniela], Castillo, A.[Angela], Arbeláez, P.[Pablo],
SEPAL: Spatial Gene Expression Prediction from Local Graphs,
CVAMD23(2286-2295)
IEEE DOI 2401
BibRef

Symeonidi, A.[Aikaterini], Nicolaou, A.[Anguelos], Johannes, F.[Frank], Christlein, V.[Vincent],
Recursive Convolutional Neural Networks for Epigenomics,
ICPR21(2567-2574)
IEEE DOI 2105
Deep learning, Genomics, Tools, Data models, Pattern recognition, Convolutional neural networks, Gene expression BibRef

Pham, C.[Chau], Pham, V.[Vung], Dang, T.[Tommy],
Genexplorer: Visualizing and Comparing Gene Expression Levels via Differential Charts,
ISVC20(I:248-259).
Springer DOI 2103
BibRef

Bombrun, M.[Maxime], Ranefall, P.[Petter], Lindblad, J.[Joakim], Allalou, A.[Amin], Partel, G.[Gabriele], Solorzano, L.[Leslie], Qian, X.Y.[Xiao-Yan], Nilsson, M.[Mats], Wählby, C.[Carolina],
Decoding Gene Expression in 2D and 3D,
SCIA17(II: 257-268).
Springer DOI 1706
BibRef

Sahoo, P., Acharya, S., Saha, S.,
Automatic generation of biclusters from gene expression data using multi-objective simulated annealing approach,
ICPR16(2174-2179)
IEEE DOI 1705
Clustering algorithms, Computer science, Gene expression, Linear programming, Simulated annealing, AMOSA, Biclustering, Gene expression data, Microarray technology, Multi-objective, optimization BibRef

Borovec, J.[Jirí], Kybic, J.[Jan],
Binary Pattern Dictionary Learning for Gene Expression Representation in Drosophila Imaginal Discs,
MCBMIIA16(II: 555-569).
Springer DOI 1704
BibRef

Adametz, D.[David], Rey, M.[Mélanie], Roth, V.[Volker],
Information Bottleneck for Pathway-Centric Gene Expression Analysis,
GCPR14(81-91).
Springer DOI 1411
BibRef

Rogowski, M., Cannistraci, C.V., Alanis-Lobato, G., Weber, P.P., Ravasi, T., Schulze, J.P., Acevedo-Feliz, D.,
Observing change in crowded data sets in 3D space: Visualizing gene expression in human tissues,
3DUI13(173-174)
IEEE DOI 1406
biology computing BibRef

Heinrich, J.[Julian], Seifert, R.[Robert], Burch, M.[Michael], Weiskopf, D.[Daniel],
BiCluster Viewer: A Visualization Tool for Analyzing Gene Expression Data,
ISVC11(I: 641-652).
Springer DOI 1109
BibRef

Kurkure, U.[Uday], Le, Y.H.[Yen H.], Paragios, N.[Nikos], Carson, J.P.[James P.], Ju, T.[Tao], Kakadiaris, I.A.[Ioannis A.],
Landmark/image-based deformable registration of gene expression data,
CVPR11(1089-1096).
IEEE DOI 1106
BibRef

Chiu, T.Y.[Tai-Yu], Hsu, T.C.[Ting-Chieh], Wang, J.S.[Jia-Shung],
AP-Based Consensus Clustering for Gene Expression Time Series,
ICPR10(2512-2515).
IEEE DOI 1008
BibRef

Myasnikova, E., Surkova, S., Samsonova, M., Reinitz, J.,
Estimation of Errors in Gene Expression Data Introduced by Diffractive Blurring of Confocal Images,
IMVIP09(53-58).
IEEE DOI 0909
BibRef

Wahid, C.M.M.[Choudhury Muhammad Mufassil], Ali, A.B.M.S.[A.B.M. Shawkat], Tickle, K.[Kevin],
Impact of Feature Selection on Support Vector Machine Using Microarray Gene Expression Data,
ICMV09(189-193).
IEEE DOI 0912
BibRef

Gong, Y.C.[Yun-Chao], Chen, C.L.[Chuan-Liang],
Semi-supervised method for gene expression data classification with Gaussian fields and harmonic functions,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Kashef, R.[Rasha], Kamel, M.S.[Mohamed S.],
Efficient Bisecting k -Medoids and Its Application in Gene Expression Analysis,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef

Okun, O.[Oleg], Priisalu, H.[Helen],
Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues,
IbPRIA07(II: 483-490).
Springer DOI 0706
BibRef

Mitra, S.[Sushmita], Banka, H.[Haider], Pal, S.K.[Sankar K.],
A MOE framework for Biclustering of Microarray Data,
ICPR06(I: 1154-1157).
IEEE DOI 0609
Gene expression data. BibRef

An, J.Y.[Ji-Yuan], Chen, Y.P.P.[Yi-Ping Phoebe],
Finding Rule Groups to Classify High Dimensional Gene Expression Datasets,
ICPR06(I: 1196-1199).
IEEE DOI 0609
BibRef

Harpaz, R.[Rave], Haralick, R.M.[Robert M.],
Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning,
ICPR06(II: 670-674).
IEEE DOI 0609
BibRef

Song, J.Z., Duan, K.M., Surette, M.,
The Wavelet Transformation for Temporal Gene Expression Analysis,
BioInfo05(III: 148-148).
IEEE DOI 0507
BibRef

Ahammad, P.[Parvez], Harmon, C.L.[Cyrus L.], Hammonds, A.[Ann], Sastry, S.S.[S. Shankar], Rubin, G.M.[Gerald M.],
Joint Nonparametric Alignment for Analyzing Spatial Gene Expression Patterns in Drosophila Imaginal Discs,
CVPR05(II: 755-760).
IEEE DOI 0507
BibRef

Fraser, K., O'Neill, P., Wang, Z.D.[Zi-Dong], Liu, X.H.[Xiao-Hui],
'Copasetic analysis': automated analysis of biological gene expression images,
ICARCV04(II: 1061-1066).
IEEE DOI 0412
BibRef

Syeda-Mahmood, T.F.,
Order-preserving clustering and its application to gene expression data,
ICPR04(IV: 637-640).
IEEE DOI 0409
BibRef

Mitra, P., Majumder, D.D.,
Feature selection and gene clustering from gene expression data,
ICPR04(II: 343-346).
IEEE DOI 0409
BibRef

Xuan, J.H.[Jian-Hua], Dong, Y.B.[Yi-Bin], Khan, J., Hoffman, E., Clarke, R., Wang, Y.[Yue],
Robust Feature Selection by Weighted Fisher Criterion for Multiclass Prediction in Gene Expression Profiling,
ICPR04(II: 291-294).
IEEE DOI 0409
BibRef

Wei, W.[Wu], Xin, L.[Liu], Min, X.[Xu], Jinrong, P.[Peng], Setiono, R.,
A hybrid SOM-SVM method for analyzing zebra fish gene expression,
ICPR04(II: 323-326).
IEEE DOI 0409
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Extraction and Analysis of Cells .


Last update:Jul 13, 2024 at 15:27:21