Network-based learning algorithms for understanding human disease

University of Minnesota Ph.D. dissertation. March 2011. Major: Computer science. Advisor: Rui Kuang, Ph.D. 1 computer file (PDF)xiv, 99 pages.

[1]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[2]  L. Chin,et al.  High-resolution genomic profiles of human lung cancer. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Robert E. Schapire,et al.  Hierarchical multi-label prediction of gene function , 2006, Bioinform..

[4]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[5]  John Blitzer,et al.  Regularized Learning with Networks of Features , 2008, NIPS.

[6]  Serge J. Belongie,et al.  Higher order learning with graphs , 2006, ICML.

[7]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

[9]  Xiaodong Lin,et al.  Gene expression Gene selection using support vector machines with non-convex penalty , 2005 .

[10]  C Caldas,et al.  High-resolution analysis of chromosome rearrangements on 8p in breast, colon and pancreatic cancer reveals a complex pattern of loss, gain and translocation , 2006, Oncogene.

[11]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[12]  James C. Bezdek,et al.  Convergence of Alternating Optimization , 2003, Neural Parallel Sci. Comput..

[13]  Petter Holme,et al.  Network Properties of Complex Human Disease Genes Identified through Genome-Wide Association Studies , 2009, PloS one.

[14]  Jingrui He,et al.  Manifold-ranking based image retrieval , 2004, MULTIMEDIA '04.

[15]  Mikhail Belkin,et al.  Using manifold structure for partially labelled classification , 2002, NIPS 2002.

[16]  Michael Gribskov,et al.  Use of Receiver Operating Characteristic (ROC) Analysis to Evaluate Sequence Matching , 1996, Comput. Chem..

[17]  Jane Fridlyand,et al.  Bladder Cancer Stage and Outcome by Array-Based Comparative Genomic Hybridization , 2005, Clinical Cancer Research.

[18]  Baldomero Oliva,et al.  Predicting cancer involvement of genes from heterogeneous data , 2008, BMC Bioinformatics.

[19]  Sergio E Baranzini,et al.  The genetics of autoimmune diseases: a networked perspective. , 2009, Current opinion in immunology.

[20]  TaeHyun Hwang,et al.  A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge , 2009, Bioinform..

[21]  Weifeng Liu,et al.  Adaptive and Learning Systems for Signal Processing, Communication, and Control , 2010 .

[22]  Michalis V. Karamouzis,et al.  Post-translational modifications and regulation of the RAS superfamily of GTPases as anticancer targets , 2007, Nature Reviews Drug Discovery.

[23]  A. Shlien,et al.  Copy number variations and cancer , 2009, Genome Medicine.

[24]  J. Foekens,et al.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.

[25]  Roded Sharan,et al.  Associating Genes and Protein Complexes with Disease via Network Propagation , 2010, PLoS Comput. Biol..

[26]  Andrew D. Johnson,et al.  Bmc Medical Genetics an Open Access Database of Genome-wide Association Results , 2009 .

[27]  Bart De Moor,et al.  Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks , 2006, ISMB.

[28]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.

[29]  Tommi S. Jaakkola,et al.  Partially labeled classification with Markov random walks , 2001, NIPS.

[30]  R. Bernards,et al.  Enabling personalized cancer medicine through analysis of gene-expression patterns , 2008, Nature.

[31]  Li Liu,et al.  Improved breast cancer prognosis through the combination of clinical and genetic markers , 2007, Bioinform..

[32]  James V. Candy,et al.  Adaptive and Learning Systems for Signal Processing, Communications, and Control , 2006 .

[33]  Hongyuan Zha,et al.  Co-ranking Authors and Documents in a Heterogeneous Network , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[34]  R. Tanzi,et al.  Thirty years of Alzheimer's disease genetics: the implications of systematic meta-analyses , 2008, Nature Reviews Neuroscience.

[35]  Fan Chung,et al.  Spectral Graph Theory , 1996 .

[36]  V. McKusick Mendelian Inheritance in Man and Its Online Version, OMIM , 2007, The American Journal of Human Genetics.

[37]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[38]  Hanno Steen,et al.  Development of human protein reference database as an initial platform for approaching systems biology in humans. , 2003, Genome research.

[39]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[40]  Vipin Kumar,et al.  Robust and efficient identification of biomarkers by classifying features on graphs , 2008, Bioinform..

[41]  Matthew A. Hibbs,et al.  Exploring the human genome with functional maps. , 2009, Genome research.

[42]  David Martin,et al.  GOToolBox: functional analysis of gene datasets based on Gene Ontology , 2004, Genome Biology.

[43]  Jagdish Chandra Patra,et al.  Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network , 2010, Bioinform..

[44]  Dale Schuurmans,et al.  Information Marginalization on Subgraphs , 2006, PKDD.

[45]  Jieping Ye,et al.  Identifying biologically relevant genes via multiple heterogeneous data sources , 2008, KDD.

[46]  Nicolas Le Roux,et al.  Label Propagation and Quadratic Criterion , 2006, Semi-Supervised Learning.

[47]  Jason Weston,et al.  Protein ranking: from local to global structure in the protein similarity network. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[48]  Ricardo Saban,et al.  Repeated BCG treatment of mouse bladder selectively stimulates small GTPases and HLA antigens and inhibits single-spanning uroplakins , 2007, BMC Cancer.

[49]  Yi Zhang,et al.  Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer , 2007, BMC Cancer.

[50]  W. Gerald,et al.  Gene expression profiling predicts clinical outcome of prostate cancer. , 2004, The Journal of clinical investigation.

[51]  A. Dupuy,et al.  Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. , 2007, Journal of the National Cancer Institute.

[52]  E. Lander,et al.  Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma , 2007, Proceedings of the National Academy of Sciences.

[53]  F. Collins,et al.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.

[54]  E. Snitkin,et al.  Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network , 2009, Genome Biology.

[55]  TaeHyun Hwang,et al.  Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[56]  Jeff Shrager,et al.  Observation of Phase Transitions in Spreading Activation Networks , 1987, Science.

[57]  Derek Y. Chiang,et al.  The landscape of somatic copy-number alteration across human cancers , 2010, Nature.

[58]  Emmanuel Barillot,et al.  Classification of arrayCGH data using fused SVM , 2008, ISMB.

[59]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[60]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[61]  D. Valle,et al.  Online Mendelian Inheritance In Man (OMIM) , 2000, Human mutation.

[62]  Justis P. Ehlers,et al.  Functional gene expression analysis uncovers phenotypic switch in aggressive uveal melanomas. , 2006, Cancer research.

[63]  G. Vriend,et al.  A text-mining analysis of the human phenome , 2006, European Journal of Human Genetics.

[64]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[65]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[66]  Bernhard Schölkopf,et al.  Fast protein classification with multiple networks , 2005, ECCB/JBI.

[67]  Emmanuel Barillot,et al.  Classification of microarray data using gene networks , 2007, BMC Bioinformatics.

[68]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[69]  Muin J. Khoury,et al.  Genetic Association Studies of Cancer: Where Do We Go from Here? , 2007, Cancer Epidemiology Biomarkers & Prevention.

[70]  P. Robinson,et al.  Walking the interactome for prioritization of candidate disease genes. , 2008, American journal of human genetics.

[71]  Michael Q. Zhang,et al.  Network-based global inference of human disease genes , 2008, Molecular systems biology.

[72]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[73]  C. Sawyers The cancer biomarker problem , 2008, Nature.

[74]  Bernhard Schölkopf,et al.  Learning with Hypergraphs: Clustering, Classification, and Embedding , 2006, NIPS.

[75]  Hongzhe Li,et al.  In Response to Comment on "Network-constrained regularization and variable selection for analysis of genomic data" , 2008, Bioinform..

[76]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[77]  Bahram Parvin,et al.  Sparse multitask regression for identifying common mechanism of response to therapeutic targets , 2010, Bioinform..

[78]  Dan Theodorescu,et al.  Profiling bladder cancer organ site-specific metastasis identifies LAMC2 as a novel biomarker of hematogenous dissemination. , 2009, The American journal of pathology.

[79]  S. Dudoit,et al.  STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .

[80]  Jason Weston,et al.  Motif-based protein ranking by network propagation , 2005, Bioinform..

[81]  TaeHyun Hwang,et al.  A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery , 2010, SDM.

[82]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[83]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[84]  Edward A. Fox,et al.  Link fusion: a unified link analysis framework for multi-type interrelated data objects , 2004, WWW '04.

[85]  C. Sabatti,et al.  The Human Phenome Project , 2003, Nature Genetics.

[86]  C. Wijmenga,et al.  Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. , 2006, American journal of human genetics.