RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2008
VO 3
IS 1
SP 2
TI Jointly analyzing gene expression and copy number data in breast cancer using data reduction models
A1 J.A. Berger,
A1 S. Hautaniemi,
A1 S.K. Mitra,
A1 J. Astola,
K1 Gene expression
K1 Breast cancer
K1 Bioinformatics
K1 Genomics
K1 DNA
K1 Biological system modeling
K1 Biological information theory
K1 Singular value decomposition
K1 Humans
K1 Proteins
K1 breast cancer.
K1 Generalized singular value decomposition
K1 cDNA microarray data
K1 CGH microarray data
K1 gene expression
K1 DNA copy numbers
AB With the growing surge of biological measurements, the problem of integrating and analyzing different types of genomic measurements has become an immediate challenge for elucidating events at the molecular level. In order to address the problem of integrating different data types, we present a framework that locates variation patterns in two biological inputs based on the generalized singular value decomposition (GSVD). In this work, we jointly examine gene expression and copy number data and iteratively project the data on different decomposition directions defined by the projection angle thetas in the GSVD. With the proper choice of thetas, we locate similar and dissimilar patterns of variation between both data types. We discuss the properties of our algorithm using simulated data and conduct a case study with biologically verified results. Ultimately, we demonstrate the efficacy of our method on two genome-wide breast cancer studies to identify genes with large variation in expression and copy number across numerous cell line and tumor samples. Our method identifies genes that are statistically significant in both input measurements. The proposed method is useful for a wide variety of joint copy number and expression-based studies. Supplementary information is available online, including software implementations and experimental data
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.10
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.10

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2008
VO 3
IS 1
SP 17
TI Spatio-temporal analysis of constitutive exocytosis in epithelial cells
A1 R. Sebastian,
A1 M.-E. Diaz,
A1 G. Ayala,
A1 K. Letinic,
A1 J. Moncho-Bogani,
A1 D. Toomre,
K1 Biomembranes
K1 Testing
K1 Biochemistry
K1 Proteins
K1 Pathology
K1 Microscopy
K1 Performance analysis
K1 Plasma applications
K1 Plasma transport processes
K1 Plasma waves
K1 total internal reflection fluorescent microscopy (TIRFM).
K1 Spatio-temporal clustering
K1 exocytosis
K1 epithelial cells
AB Exocytosis is an essential cellular trafficking process integral to the proper distribution and function of a plethora of molecules, including transporters, receptors, and enzymes. Moreover, incorrect protein targeting can lead to pathological conditions. Recently, the application of evanescent wave microscopy has allowed us to image the final steps of exocytosis. However, spatio-temporal analysis of fusion of constitutive vesicular traffic with the plasma membrane has not been systematically performed. Also, the spatial sites and times of vesicle fusion have not yet been analyzed together. In addition, more formal tests are required in testing biological hypotheses, rather than visual inspection combined with statistical descriptives. Ripley K-functions are used to examine the joint and marginal behavior of locations and fusion times. Semiautomatic detection and mapping of constitutive fusion sites reveals spatial and temporal clustering, but no dependency between the locations and times of fusion events. Our novel approach could be translated to other studies of membrane trafficking in health and diseases such as diabetes
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.11
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.11

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2008
VO 3
IS 1
SP 33
TI Statistical analysis of RNA backbone
A1 E. Hershkovitz,
A1 G. Sapiro,
A1 A. Tannenbaum,
A1 L.D. Williams,
K1 Statistical analysis
K1 RNA
K1 Spine
K1 Proteins
K1 Frequency
K1 Vector quantization
K1 Polymers
K1 Databases
K1 Information analysis
K1 Statistics
K1 conformational motifs.
K1 RNA backbone
K1 statistical analysis
K1 vector quantization
K1 local conformations
K1 torsion angles
AB Local conformation is an important determinant of RNA catalysis and binding. The analysis of RNA conformation is particularly difficult due to the large number of degrees of freedom (torsion angles) per residue. Proteins, by comparison, have many fewer degrees of freedom per residue. In this work, we use and extend classical tools from statistics and signal processing to search for clusters in RNA conformational space. Results are reported both for scalar analysis, where each torsion angle is separately studied, and for vectorial analysis, where several angles are simultaneously clustered. Adapting techniques from vector quantization and clustering to the RNA structure, we find torsion angle clusters and RNA conformational motifs. We validate the technique using well-known conformational motifs, showing that the simultaneous study of the total torsion angle space leads to results consistent with known motifs reported in the literature and also to the finding of new ones
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.13
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.13

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2008
VO 3
IS 1
SP 47
TI Gene mapping and marker clustering using Shannon's mutual information
A1 Z. Dawy,
A1 B. Goebel,
A1 J. Hagenauer,
A1 C. Andreoli,
A1 T. Meitinger,
A1 J.C. Mueller,
K1 Mutual information
K1 Diseases
K1 Clustering algorithms
K1 Humans
K1 Information theory
K1 Genetic communication
K1 Information analysis
K1 Entropy
K1 Multidimensional systems
K1 Visualization
K1 SNPs.
K1 Complex traits
K1 genotype-phenotype association
K1 information theory
K1 relevance chains
AB Finding the causal genetic regions underlying complex traits is one of the main aims in human genetics. In the context of complex diseases, which are believed to be controlled by multiple contributing loci of largely unknown effect and position, it is especially important to develop general yet sensitive methods for gene mapping. We discuss the use of Shannon's information theory for population-based gene mapping of discrete and quantitative traits and for marker clustering. Various measures of mutual information were employed in order to develop a comprehensive framework for gene mapping analyses. An algorithm aimed at finding so-called relevance chains of causal markers is proposed. Moreover, entropy measures are used in conjunction with multidimensional scaling to visualize clusters of genetic markers. The relevance chain algorithm successfully detected the two causal regions in a simulated scenario. The approach has also been applied to a published clinical study on autoimmune (Graves') disease. Results were consistent with those of standard statistical methods, but identified an additional locus of interest in the promoter region of the associated gene CTLA4. The developed software is freely available at http://www.lnt.ei.tum.de/download/InfoGeneMap/
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.9
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.9

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2008
VO 3
IS 1
SP 57
TI A hidden Markov model for transcriptional regulation in single cells
K1 Hidden Markov models
K1 Stochastic systems
K1 Biological system modeling
K1 Biological information theory
K1 Stochastic processes
K1 Predictive models
K1 Evolution (biology)
K1 Biology computing
K1 Computational modeling
K1 Cells (biology)
K1 transcriptional regulatory systems.
K1 Hidden Markov models
K1 Monte Carlo simulation
K1 stochastic biochemical systems
K1 stochastic dynamical systems
K1 transcriptional regulation
AB We discuss several issues pertaining to the use of stochastic biochemical systems for modeling transcriptional regulation in single cells. By appropriately choosing the system state, we can model transcriptional regulation by a hidden Markov model (HMM). This opens the possibility of using well-known techniques for the statistical analysis and stochastic control of HMMs to mathematically and computationally study transcriptional regulation in single cells. Unfortunately, in all but a few simple cases, analytical characterization of the statistical behavior of the proposed HMM is not possible. Moreover, analysis by Monte Carlo simulation is computationally cumbersome. We discuss several techniques for approximating the HMM by one that is more tractable. We employ simulations, based on a biologically relevant transcriptional regulatory system, to show the relative merits and limitations of various approximation techniques and provide general guidelines for their use
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.2
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.2

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2008
VO 3
IS 1
SP 72
TI A hill-climbing approach for automatic gridding of cDNA microarray images
A1 L. Rueda,
A1 V. Vidyadharan,
K1 Statistical analysis
K1 Data mining
K1 Image analysis
K1 Contamination
K1 Noise generators
K1 Mesh generation
K1 Image segmentation
K1 Boundary conditions
K1 Image databases
K1 Genomics
K1 hill-climbing.
K1 cDNA Microarrays
K1 gene expression
K1 gridding
AB Image and statistical analysis are two important stages of cDNA microarrays. Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment used to print the arrays, rotations, misalignments, high contamination with noise and artifacts, and the enormous amount of data generated, solving the gridding problem by means of an automatic system is not trivial. Existing techniques to solve the automatic grid segmentation problem cover only limited aspects of this challenging problem and require the user to specify the size of the spots, the number of rows and columns in the grid, and boundary conditions. In this paper, a hill-climbing automatic gridding and spot quantification technique is proposed which takes a microarray image (or a subgrid) as input and makes no assumptions about the size of the spots, rows, and columns in the grid. The proposed method is based on a hill-climbing approach that utilizes different objective functions. The method has been found to effectively detect the grids on microarray images drawn from databases from GEO and the Stanford genomic laboratories
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.3
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.3

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2008
VO 3
IS 1
SP 84
TI Unicyclic networks: compatibility and enumeration
A1 C. Semple,
A1 M. Steel,
K1 Phylogeny
K1 Displays
K1 Tree graphs
K1 Evolution (biology)
K1 Bioinformatics
K1 Biological system modeling
K1 Genomics
K1 Steel
K1 Closed-form solution
K1 Biological processes
K1 galled-trees.
K1 Phylogenetic tree
K1 compatibility
K1 circular orderings
K1 generating function
AB Graphs obtained from a binary leaf labeled ("phylogenetic") tree by adding an edge so as to introduce a cycle provide a useful representation of hybrid evolution in molecular evolutionary biology. This class of graphs (which we call "unicyclic networks") also has some attractive combinatorial properties, which we present. We characterize when a set of binary phylogenetic trees is displayed by a unicyclic network in terms of tree rearrangement operations. This leads to a triple-wise compatibility theorem and a simple, fast algorithm to determine 1-cycle compatibility. We also use generating function techniques to provide closed-form expressions that enumerate unicyclic networks with specified or unspecified cycle length, and we provide an extension to enumerate a class of multicyclic networks
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.14
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.14

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2008
VO 3
IS 1
SP 92
TI A short proof that phylogenetic tree reconstruction by maximum likelihood is hard
K1 Phylogeny
K1 Steel
K1 Probability
K1 History
K1 Biology computing
K1 Statistics
K1 Genetics
K1 Sequences
K1 Systematics
K1 Evolution (biology)
K1 biology and genetics.
K1 Analysis of algorithms and problem complexity
K1 probability and statistics
AB Maximum likelihood is one of the most widely used techniques to infer evolutionary histories. Although it is thought to be intractable, a proof of its hardness has been lacking. Here, we give a short proof that computing the maximum likelihood tree is NP-hard by exploiting a connection between likelihood and parsimony observed by Tuffley and Steel
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.4
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.4

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 47
TI Gene Mapping and Marker Clustering Using Shannon's Mutual Information
A1 Thomas Meitinger,
A1 Zaher Dawy,
A1 Jakob C. Mueller,
A1 Christophe Andreoli,
A1 Bernhard Goebel,
A1 Joachim Hagenauer,
K1 Complex traits
K1 genotype-phenotype association
K1 information theory
K1 relevance chains
K1 SNPs.
AB Finding the causal genetic regions underlying complex traits is one of the main aims in human genetics. In the context of complex diseases, which are believed to be controlled by multiple contributing loci of largely unknown effect and position, it is especially important to develop general yet sensitive methods for gene mapping. We discuss the use of Shannon's information theory for population-based gene mapping of discrete and quantitative traits and for marker clustering. Various measures of mutual information were employed in order to develop a comprehensive framework for gene mapping analyses. An algorithm aimed at finding so-called relevance chains of causal markers is proposed. Moreover, entropy measures are used in conjunction with multidimensional scaling to visualize clusters of genetic markers. The relevance chain algorithm successfully detected the two causal regions in a simulated scenario. The approach has also been applied to a published clinical study on autoimmune (Graves') disease. Results were consistent with those of standard statistical methods, but identified an additional locus of interest in the promotor region of the associated gene CTLA4. The developed software is freely available at http://www.lnt.ei.tum.de/download/InfoGeneMap/.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.9
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.9

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 1
TI State of the Journal
A1 Dan Gusfield,
K1
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.12
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.12

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 33
TI Statistical Analysis of RNA Backbone
A1 Eli Hershkovitz,
A1 Allen Tannenbaum,
A1 Guillermo Sapiro,
A1 Loren Dean Williams,
K1 RNA backbone
K1 statistical analysis
K1 vector quantization
K1 local conformations
K1 torsion angles
K1 conformational motifs.
AB Local conformation is an important determinant of RNA catalysis and binding. The analysis of RNA conformation is particularly difficult due to the large number of degrees of freedom (torsion angles) per residue. Proteins, by comparison, have many fewer degrees of freedom per residue. In this work, we use and extend classical tools from statistics and signal processing to search for clusters in RNA conformational space. Results are reported both for scalar analysis, where each torsion angle is separately studied, and for vectorial analysis, where several angles are simultaneously clustered. Adapting techniques from vector quantization and clustering to the RNA structure, we find torsion angle clusters and RNA conformational motifs. We validate the technique using well-known conformational motifs, showing that the simultaneous study of the total torsion angle space leads to results consistent with known motifs reported in the literature and also to the finding of new ones.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.13
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.13

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 92
TI A Short Proof that Phylogenetic Tree Reconstruction by Maximum Likelihood Is Hard
A1 Sebastien Roch,
K1 Analysis of algorithms and problem complexity
K1 probability and statistics
K1 biology and genetics.
AB Maximum likelihood is one of the most widely used techniques to infer evolutionary histories. Although it is thought to be intractable, a proof of its hardness has been lacking. Here, we give a short proof that computing the maximum likelihood tree is NP-hard by exploiting a connection between likelihood and parsimony observed by Tuffley and Steel.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.4
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.4

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 95
TI 2005 Reviewers List
K1
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.1
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.1

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 17
TI Spatio-Temporal Analysis of Constitutive Exocytosis in Epithelial Cells
A1 Mar?a-Elena Diaz,
A1 Guillermo Ayala,
A1 Jose Moncho-Bogani,
A1 Derek Toomre,
A1 Rafael Sebastian,
A1 Kresimir Letinic,
K1 Spatio-temporal clustering
K1 exocytosis
K1 epithelial cells
K1 total internal reflection fluorescent microscopy (TIRFM).
AB Exocytosis is an essential cellular trafficking process integral to the proper distribution and function of a plethora of molecules, including transporters, receptors, and enzymes. Moreover, incorrect protein targeting can lead to pathological conditions. Recently, the application of evanescent wave microscopy has allowed us to image the final steps of exocytosis. However, spatio-temporal analysis of fusion of constitutive vesicular traffic with the plasma membrane has not been systematically performed. Also, the spatial sites and times of vesicle fusion have not yet been analyzed together. In addition, more formal tests are required in testing biological hypotheses, rather than visual inspection combined with statistical descriptives. Ripley {\cal K}{\hbox{-}}{\rm functions} are used to examine the joint and marginal behavior of locations and fusion times. Semiautomatic detection and mapping of constitutive fusion sites reveals spatial and temporal clustering, but no dependency between the locations and times of fusion events. Our novel approach could be translated to other studies of membrane trafficking in health and diseases such as diabetes.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.11
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.11

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 84
TI Unicyclic Networks: Compatibility and Enumeration
A1 Mike Steel,
A1 Charles Semple,
K1 Phylogenetic tree
K1 compatibility
K1 circular orderings
K1 generating function
K1 galled-trees.
AB Graphs obtained from a binary leaf labeled ("phylogenetic”) tree by adding an edge so as to introduce a cycle provide a useful representation of hybrid evolution in molecular evolutionary biology. This class of graphs (which we call "unicyclic networks”) also has some attractive combinatorial properties, which we present. We characterize when a set of binary phylogenetic trees is displayed by a unicyclic network in terms of tree rearrangement operations. This leads to a triple-wise compatibility theorem and a simple, fast algorithm to determine 1{\hbox{-}}{\rm cycle} compatibility. We also use generating function techniques to provide closed-form expressions that enumerate unicyclic networks with specified or unspecified cycle length, and we provide an extension to enumerate a class of multicyclic networks.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.14
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.14

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 57
TI A Hidden Markov Model for Transcriptional Regulation in Single Cells
A1 John Goutsias,
K1 Hidden Markov models
K1 Monte Carlo simulation
K1 stochastic biochemical systems
K1 stochastic dynamical systems
K1 transcriptional regulation
K1 transcriptional regulatory systems.
AB We discuss several issues pertaining to the use of stochastic biochemical systems for modeling transcriptional regulation in single cells. By appropriately choosing the system state, we can model transcriptional regulation by a hidden Markov model (HMM). This opens the possibility of using well-known techniques for the statistical analysis and stochastic control of HMMs to mathematically and computationally study transcriptional regulation in single cells. Unfortunately, in all but a few simple cases, analytical characterization of the statistical behavior of the proposed HMM is not possible. Moreover, analysis by Monte Carlo simulation is computationally cumbersome. We discuss several techniques for approximating the HMM by one that is more tractable. We employ simulations, based on a biologically relevant transcriptional regulatory system, to show the relative merits and limitations of various approximation techniques and provide general guidelines for their use.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.2
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.2

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 72
TI A Hill-Climbing Approach for Automatic Gridding of cDNA Microarray Images
A1 Luis Rueda,
A1 Vidya Vidyadharan,
K1 cDNA Microarrays
K1 gene expression
K1 gridding
K1 hill-climbing.
AB Image and statistical analysis are two important stages of cDNA microarrays. Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment used to print the arrays, rotations, misalignments, high contamination with noise and artifacts, and the enormous amount of data generated, solving the gridding problem by means of an automatic system is not trivial. Existing techniques to solve the automatic grid segmentation problem cover only limited aspects of this challenging problem and require the user to specify the size of the spots, the number of rows and columns in the grid, and boundary conditions. In this paper, a hill-climbing automatic gridding and spot quantification technique is proposed which takes a microarray image (or a subgrid) as input and makes no assumptions about the size of the spots, rows, and columns in the grid. The proposed method is based on a hill-climbing approach that utilizes different objective functions. The method has been found to effectively detect the grids on microarray images drawn from databases from GEO and the Stanford genomic laboratories.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.3
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.3

RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2006
VO 3
IS
SP 2
TI Jointly Analyzing Gene Expression and Copy Number Data in Breast Cancer Using Data Reduction Models
A1 Sanjit K. Mitra,
A1 Sampsa Hautaniemi,
A1 Jaakko Astola,
A1 John A. Berger,
K1 Generalized singular value decomposition
K1 cDNA microarray data
K1 CGH microarray data
K1 gene expression
K1 DNA copy numbers
K1 breast cancer.
AB With the growing surge of biological measurements, the problem of integrating and analyzing different types of genomic measurements has become an immediate challenge for elucidating events at the molecular level. In order to address the problem of integrating different data types, we present a framework that locates variation patterns in two biological inputs based on the generalized singular value decomposition (GSVD). In this work, we jointly examine gene expression and copy number data and iteratively project the data on different decomposition directions defined by the projection angle \theta in the GSVD. With the proper choice of \theta, we locate similar and dissimilar patterns of variation between both data types. We discuss the properties of our algorithm using simulated data and conduct a case study with biologically verified results. Ultimately, we demonstrate the efficacy of our method on two genome-wide breast cancer studies to identify genes with large variation in expression and copy number across numerous cell line and tumor samples. Our method identifies genes that are statistically significant in both input measurements. The proposed method is useful for a wide variety of joint copy number and expression-based studies. Supplementary information is available online, including software implementations and experimental data.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2006.10
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.10