Module-Based Knowledge Discovery for Multiple-Cytosine-Variant Methylation Profile

[1]  Saurav Mallik,et al.  Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  M. Bhattacharyya,et al.  Identifying microRNAs related to Alzheimer's disease from differential methylation signatures , 2016 .

[3]  Ujjwal Maulik,et al.  IDPT: Insights into potential intrinsically disordered proteins through transcriptomic analysis of genes for prostate carcinoma epigenetic data. , 2016, Gene.

[4]  Hsien-Da Huang,et al.  miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database , 2015, Nucleic Acids Res..

[5]  L. Alfredsson,et al.  The Association between Job Strain and Atrial Fibrillation: Results from the Swedish WOLF Study , 2015, BioMed research international.

[6]  Ujjwal Maulik,et al.  MiRNA-TF-gene network analysis through ranking of biomolecules for multi-informative uterine leiomyoma dataset , 2015, J. Biomed. Informatics.

[7]  Volkhard Helms,et al.  Integrative network-based approach identifies key genetic elements in breast invasive carcinoma , 2015, BMC Genomics.

[8]  Ujjwal Maulik,et al.  Transcriptomic Analysis of mRNAs in Human Monocytic Cells Expressing the HIV-1 Nef Protein and Their Exosomes , 2015, BioMed research international.

[9]  Ujjwal Maulik,et al.  Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining , 2015, PloS one.

[10]  Mathieu Blanchette,et al.  The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts , 2014, Genome Biology.

[11]  J. Garssen,et al.  Recovery of extracellular vesicles from human breast milk is influenced by sample collection and vesicle isolation procedures , 2014, Journal of extracellular vesicles.

[12]  Ujjwal Maulik,et al.  The HIV Nef protein modulates cellular and exosomal miRNA profiles in human monocytic cells , 2014, Journal of extracellular vesicles.

[13]  L. Williams,et al.  Early Exposure to Traumatic Stressors Impairs Emotional Brain Circuitry , 2013, PloS one.

[14]  Fabian J. Theis,et al.  A modular framework for gene set analysis integrating multilevel omics data , 2013, Nucleic acids research.

[15]  S. Plevritis,et al.  Identification of ovarian cancer driver genes by using module network integration of multi-omics data , 2013, Interface Focus.

[16]  Thomas Lengauer,et al.  DNA Methylation Patterns Facilitate the Identification of MicroRNA Transcription Start Sites: A Brain-Specific Study , 2013, PloS one.

[17]  Ujjwal Maulik,et al.  Integrated analysis of gene expression and genome-wide DNA methylation for tumor prediction: An association rule mining-based approach , 2013, 2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

[18]  Edward Y. Chen,et al.  Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool , 2013, BMC Bioinformatics.

[19]  Ujjwal Maulik,et al.  Integrated Statistical and Rule-Mining Techniques for Dna Methylation and Gene Expression Data Analysis , 2013, J. Artif. Intell. Soft Comput. Res..

[20]  Sanghamitra Bandyopadhyay,et al.  A New Approach for Combining Knowledge From Multiple Coexpression Networks of MicroRNAs , 2013, IEEE Transactions on Biomedical Engineering.

[21]  Sanghamitra Bandyopadhyay,et al.  Studying the differential co-expression of microRNAs reveals significant role of white matter in early Alzheimer's progression. , 2013, Molecular bioSystems.

[22]  Adam K. L. Wong,et al.  miRNA_Targets: a database for miRNA target predictions in coding and non-coding regions of mRNAs. , 2012, Genomics.

[23]  S. Horvath,et al.  Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects , 2012, BMC Genomics.

[24]  Nathan C. Sheffield,et al.  The accessible chromatin landscape of the human genome , 2012, Nature.

[25]  Y. Shi,et al.  Tet family proteins and 5-hydroxymethylcytosine in development and disease , 2012, Development.

[26]  Hao Wu,et al.  Regulation and function of mammalian DNA methylation patterns: a genomic perspective. , 2012, Briefings in functional genomics.

[27]  M. Bhattacharyya.  Mining Co-expression Graphs: Applications to MicroRNA Regulation and Disease Analysis , 2012 .

[28]  M. Bhattacharyya.  Co-expression Toggling of MicroRNAs in Alzheimer’s Brain , 2012 .

[29]  M. Rubin,et al.  DNA unwinding by ASCC3 helicase is coupled to ALKBH3-dependent DNA alkylation repair and cancer cell proliferation. , 2011, Molecular cell.

[30]  Norbert Gretz,et al.  miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes , 2011, J. Biomed. Informatics.

[31]  Chuan He,et al.  Tet Proteins Can Convert 5-Methylcytosine to 5-Formylcytosine and 5-Carboxylcytosine , 2011, Science.

[32]  Yang Wang,et al.  Tet-Mediated Formation of 5-Carboxylcytosine and Its Excision by TDG in Mammalian DNA , 2011, Science.

[33]  Vladimir Batagelj,et al.  Fast algorithms for determining (generalized) core groups in social networks , 2011, Adv. Data Anal. Classif..

[34]  Sanghamitra Bandyopadhyay,et al.  A Biologically Inspired Measure for Coexpression Analysis , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[35]  Nectarios Koziris,et al.  DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association , 2011, Nucleic Acids Res..

[36]  N. Heintz,et al.  The Nuclear DNA Base 5-Hydroxymethylcytosine Is Present in Purkinje Neurons and the Brain , 2009, Science.

[37]  David R. Liu,et al.  Conversion of 5-Methylcytosine to 5- Hydroxymethylcytosine in Mammalian DNA by the MLL Partner TET1 , 2009 .

[38]  H. Cedar,et al.  Linking DNA methylation and histone modification: patterns and paradigms , 2009, Nature Reviews Genetics.

[39]  Sanghamitra Bandyopadhyay,et al.  Integration of Co-expression Networks for Gene Clustering , 2009, 2009 Seventh International Conference on Advances in Pattern Recognition.

[40]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[41]  Ker-Chau Li,et al.  Patterns of co-expression for protein complexes by size in Saccharomyces cerevisiae , 2008, Nucleic acids research.

[42]  Jeannie T. Lee,et al.  X chromosome dosage compensation: how mammals keep the balance. , 2008, Annual review of genetics.

[43]  Qing Yang,et al.  ITFP: an integrated platform of mammalian transcription factors , 2008, Bioinform..

[44]  K. K. Jose,et al.  Statistical tests for identification of differentially expressed genes in cDNA microarray experiments , 2008 .

[45]  Dragomir R. Radev,et al.  Identifying gene-disease associations using centrality on a literature mined gene-interaction network , 2008, ISMB.

[46]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[47]  Peter Langfelder,et al.  Eigengene networks for studying the relationships between co-expression modules , 2007, BMC Systems Biology.

[48]  Michael Kertesz,et al.  The role of site accessibility in microRNA target recognition , 2007, Nature Genetics.

[49]  Paul S. Freemont,et al.  Engineering a synthetic molecular oscillator based on the Lotka-Volterra dynamic , 2007, BMC Systems Biology.

[50]  H. Büning,et al.  Jarque–Bera Test and its Competitors for Testing Normality – A Power Comparison , 2007 .

[51]  Debashis Ghosh,et al.  Eigengene-based linear discriminant model for tumor classification using gene expression microarray data , 2006, Bioinform..

[52]  Jan Krüger,et al.  RNAhybrid: microRNA target prediction easy, fast and flexible , 2006, Nucleic Acids Res..

[53]  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.

[54]  J. A. Rodríguez-Velázquez,et al.  Subgraph centrality in complex networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[55]  G. Zocchi,et al.  Local cooperativity mechanism in the DNA melting transition. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[56]  K. Gunsalus,et al.  Combinatorial microRNA target predictions , 2005, Nature Genetics.

[57]  Anton J. Enright,et al.  Human MicroRNA Targets , 2004, PLoS biology.

[58]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[59]  C. Burge,et al.  Prediction of Mammalian MicroRNA Targets , 2003, Cell.

[60]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[61]  T. Hashimshony,et al.  The role of DNA methylation in setting up chromatin structure during development , 2003, Nature Genetics.

[62]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[63]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[64]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[65]  Phillip Bonacich,et al.  Eigenvector-like measures of centrality for asymmetric relations , 2001, Soc. Networks.

[66]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[67]  Marti J. Anderson,et al.  Permutation tests for univariate or multivariate analysis of variance and regression , 2001 .

[68]  A. Bird,et al.  Non-CpG methylation is prevalent in embryonic stem cells and may be mediated by DNA methyltransferase 3a. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[69]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[70]  M. Weigt,et al.  On the properties of small-world network models , 1999, cond-mat/9903411.

[71]  Stefan U Kass,et al.  DNA methylation directs a time-dependent repression of transcription initiation , 1997, Current Biology.

[72]  Rudolf Jaenisch,et al.  Role for DNA methylation in genomic imprinting , 1993, Nature.

[73]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[74]  G. R. Wyatt,et al.  The bases of the nucleic acids of some bacterial and animal viruses: the occurrence of 5-hydroxymethylcytosine. , 1953, The Biochemical journal.

[75]  Ujjwal Maulik,et al.  Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data , 2017, IEEE Transactions on NanoBioscience.

[76]  Ujjwal Maulik,et al.  RANWAR: Rank-Based Weighted Association Rule Mining From Gene Expression and Methylation Data , 2015, IEEE Transactions on NanoBioscience.

[77]  Anirban Mukhopadhyay,et al.  A Survey and Comparative Study of Statistical Tests for Identifying Differential Expression from Microarray Data , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

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

[79]  B. Ramsahoye,et al.  DNA methylation in mouse embryonic stem cells and development , 2007, Cell and Tissue Research.

[80]  Wyeth W. Wasserman,et al.  JASPAR: an open-access database for eukaryotic transcription factor binding profiles , 2004, Nucleic Acids Res..

[81]  H. Büning,et al.  Jarque-Bera test and its competitors for testing normality , 2004 .

[82]  P. Jones,et al.  The DNA methylation paradox. , 1999, Trends in genetics : TIG.

[83]  J. Herman,et al.  Alterations in DNA methylation: a fundamental aspect of neoplasia. , 1998, Advances in cancer research.

[84]  Holger Karas,et al.  TRANSFAC: a database on transcription factors and their DNA binding sites , 1996, Nucleic Acids Res..

[85]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .