DNA meets the SVD

This paper introduces an important area of computational cell biology where complex, publicly available genomic data is being examined by linear algebra methods, with the aim of revealing biological and medical insights.

[1]  Edward J. Wood.  The Oxford dictionary of biochemistry and molecular biology (second edition) , 2007 .

[2]  Peter Grindrod,et al.  Review of uses of network and graph theory concepts within proteomics , 2004, Expert review of proteomics.

[3]  Douglas M. Hawkins,et al.  Inferential, robust non-negative matrix factorization analysis of microarray data , 2007, Bioinform..

[4]  Desmond J. Higham,et al.  ANALYSIS OF THE SINGULAR VALUE DECOMPOSITION AS A TOOL FOR PROCESSING MICROARRAY EXPRESSION DATA , 2005 .

[5]  P. Uetz,et al.  What do we learn from high-throughput protein interaction data? , 2004, Expert review of proteomics.

[6]  Gene H. Golub,et al.  Matrix Computations, Third Edition , 1996 .

[7]  D. Botstein,et al.  Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[8]  A. Spence,et al.  The Sensitivity of Spectral Clustering Applied to Gene Expression Data , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[9]  Sangsoo Kim,et al.  Gene expression Differential coexpression analysis using microarray data and its application to human cancer , 2005 .

[10]  J. Rothberg,et al.  Gaining confidence in high-throughput protein interaction networks , 2004, Nature Biotechnology.

[11]  Philip A. Knight,et al.  The Sinkhorn-Knopp Algorithm: Convergence and Applications , 2008, SIAM J. Matrix Anal. Appl..

[12]  Hsinchun Chen,et al.  A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana , 2006, Bioinform..

[13]  James R. Knight,et al.  A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.

[14]  Raphael Gottardo,et al.  Flexible empirical Bayes models for differential gene expression , 2007, Bioinform..

[15]  Steven Skiena,et al.  Analysis techniques for microarray time-series data , 2001, RECOMB.

[16]  Guillermo Ricardo Simari,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[17]  David J. Buttler,et al.  Encyclopedia of Data Warehousing and Mining Second Edition , 2008 .

[18]  Michael P H Stumpf,et al.  Complex networks and simple models in biology , 2005, Journal of The Royal Society Interface.

[19]  Desmond J. Higham,et al.  A lock-and-key model for protein-protein interactions , 2006, Bioinform..

[20]  Gabriela Kalna,et al.  Spectral analysis of two-signed microarray expression data. , 2007, Mathematical medicine and biology : a journal of the IMA.

[21]  David M. Kramer,et al.  Biochemistry and Molecular Biology , 1968, Nature.

[22]  Gary Hardiman,et al.  Microarray platforms--comparisons and contrasts. , 2004, Pharmacogenomics.

[23]  Steven Skiena,et al.  Analysis Techniques for Microarray Time-Series Data , 2002, J. Comput. Biol..

[24]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[25]  Jacques Cohen,et al.  Bioinformatics—an introduction for computer scientists , 2004, CSUR.

[26]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[27]  P. Grindrod.  Range-dependent random graphs and their application to modeling large small-world Proteome datasets. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  P. Grindrod.  Systems Biology: unravelling complex networks? , 2006 .

[29]  Helen Parkinson,et al.  A quick introduction to elements of biology - cells, molecules, genes, functional genomics, microarrays , 2008 .