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Temporal and structural analysis of biological networks in combination with microarray data

Chang Hun You, Lawrence B. Holder, Diane J. Cook
2008 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology  
But our dynamic graph-based relational learning approach describes how the graphs temporally and structurally change over time in the dynamic graph representing biological networks in combination with  ...  Most approaches for analysis of microarray data disregard structural properties on biological systems.  ...  First, we discuss several works related to structural and temporal analysis of biological networks including microarray analysis and several computational methods.  ... 
doi:10.1109/cibcb.2008.4675760 dblp:conf/cibcb/YouHC08 fatcat:5ultfcy2hbg25ab6rjnuqfsg2m

Network Analysis of Epidermal Growth Factor Signaling Using Integrated Genomic, Proteomic and Phosphorylation Data

Katrina M. Waters, Tao Liu, Ryan D. Quesenberry, Alan R. Willse, Somnath Bandyopadhyay, Loel E. Kathmann, Thomas J. Weber, Richard D. Smith, H. Steven Wiley, Brian D. Thrall, Mariko Okada (Hatakeyama)
2012 PLoS ONE  
factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies.  ...  A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot.  ...  Merging the microarray, LC-FTICR and PowerBlot data also increased the overall connectivity of the networks, with 34% of the nodes included in the largest network cluster compared to only 16% with microarray  ... 
doi:10.1371/journal.pone.0034515 pmid:22479638 pmcid:PMC3315547 fatcat:x6lfjh4vxfbqnld73gbycrbfce

Computational dynamic approaches for temporal omics data with applications to systems medicine

Yulan Liang, Arpad Kelemen
2017 BioData Mining  
In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working  ...  Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations.  ...  Availability of data and materials Data sharing not applicable to this article as no datasets were generated or analysed during the current study.  ... 
doi:10.1186/s13040-017-0140-x pmid:28638442 pmcid:PMC5473988 fatcat:rscvtjlpgrf53fbwlt6t4i22em

Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model [chapter]

Martin Ehler, Vinodh Rajapakse, Barry Zeeberg, Brian Brooks, Jacob Brown, Wojciech Czaja, Robert F. Bonner
2010 Lecture Notes in Computer Science  
The combination of LCM of embryonic organs, gene expression microarrays, and extracting spatial and temporal co-variations appear to be a powerful approach to understanding the gene regulatory networks  ...  Our new method provided greater biological specificity than classical clustering algorithms in terms of identifying more biological processes and functions related to eye development as defined by Gene  ...  Acknowledgments The research was funded by the Intramural Research Program of NICHD/NIH, by NSF (CBET0854233), by NGA (HM15820810009), and by ONR (N000140910144).  ... 
doi:10.1007/978-3-642-13078-6_6 fatcat:v64x6jsypjdqphb5pkhd47g3u4

Nonlinear gene cluster analysis with labeling for microarray gene expression data in organ development

Martin Ehler, Vinodh N Rajapakse, Barry R Zeeberg, Brian P Brooks, Jacob Brown, Wojciech Czaja, Robert F Bonner
2011 BMC Proceedings  
Conclusions: The combination of LCM of embryonic organs, gene expression microarrays, and nonlinear dimension reduction with labeling is a potentially useful approach to extract subtle spatial and temporal  ...  Our results motivate further analysis of nonlinear dimension reduction with labeling within other microarray data sets from LCM dissected tissues or other cell specific samples to determine the more general  ...  Acknowledgements The research was funded by intramural research funds from the National Institute of Child Health and Human Development, National Institutes of Health.  ... 
doi:10.1186/1753-6561-5-s2-s3 pmid:21554761 pmcid:PMC3090761 fatcat:ufxu4wvcdngaznjcxd6eobbfle

Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks

Yi Kan Wang, Daniel G. Hurley, Santiago Schnell, Cristin G. Print, Edmund J. Crampin, Shyamal D. Peddada
2013 PLoS ONE  
We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steadystate and time-series gene expression data.  ...  Our results suggest that the combination of steady-state and timeseries datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified  ...  Biological enrichment analysis for the top 10 hubs in the ssMIKANA, tsMIKANA and cMIKANA network models.  ... 
doi:10.1371/journal.pone.0072103 pmid:23967277 pmcid:PMC3743784 fatcat:jiamcojr7baj7ek6spz7rikdau

Identification of temporal association rules from time-series microarray data sets

Hojung Nam, KiYoung Lee, Doheon Lee
2009 BMC Bioinformatics  
Conclusion: In this work, we proposed TARM, which is an applied form of conventional ARM. TARM showed higher precision score than Dynamic Bayesian network and Bayesian network.  ...  From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific  ...  M10309020000-03B5002-00000) and the National Research Lab. Program  ... 
doi:10.1186/1471-2105-10-s3-s6 pmid:19344482 pmcid:PMC2665054 fatcat:szjavispcfa47ltlso2eg4wlhi

On the way toward systems biology of Aspergillus fumigatus infection

Daniela Albrecht, Olaf Kniemeyer, Franziska Mech, Matthias Gunzer, Axel Brakhage, Reinhard Guthke
2011 International Journal of Medical Microbiology  
The data analysis workflow starts with pre-processing including imputing of missing values and normalization.  ...  Sequence data and other prior knowledge extracted from databases are integrated to support the inference of gene regulatory networks associated with pathogenicity.  ...  Acknowledgements This work was supported by the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1160 'Colonisation and infection by human-pathogenic fungi'.  ... 
doi:10.1016/j.ijmm.2011.04.014 pmid:21555243 fatcat:4aoxdyjprfcu5cu56h74fqo53u

Transcriptional networks — crops, clocks, and abiotic stress

Malia A Gehan, Kathleen Greenham, Todd C Mockler, C Robertson McClung
2015 Current opinion in plant biology  
To leverage these strategies for producing the next generation of crops, future transcriptomic data acquisition should be pursued with an appropriate temporal design and analyzed with a network-centric  ...  The following review focuses on recent developments in abiotic stress transcriptional networks in economically important crops and will highlight the utility of correlation-based network analysis and applications  ...  This work was supported in part by grants from the Department of Energy (DE-SC0012639 and DE-SC0008769 to TCM), the National Science Foundation (IOS-1202682 to MAG, IOS-1202779 to KG, IOS-1127017 to TCM  ... 
doi:10.1016/j.pbi.2015.01.004 pmid:25646668 fatcat:go2hm47qtbailgfv7whdhsa5m4

A Bayesian framework for combining heterogeneous data sources for gene function prediction (inSaccharomyces cerevisiae)

Olga G. Troyanskaya, Kara Dolinski, Art B. Owen, Russ B. Altman, David Botstein
2003 Proceedings of the National Academy of Sciences of the United States of America  
We found that by creating functional groupings based on heterogeneous data types, MAGIC improved accuracy of the groupings compared with microarray analysis alone.  ...  We applied MAGIC to Saccharomyces cerevisiae genetic and physical interactions, microarray, and transcription factor binding sites data and assessed the biological relevance of gene groupings using Gene  ...  We thank all of the Saccharomyces Genome Database curators for their input into the Bayesian network, and the GRID database staff for providing their data.  ... 
doi:10.1073/pnas.0832373100 pmid:12826619 pmcid:PMC166232 fatcat:txdhacvjp5gnbf6mt6fqntwkzi

Network reconstruction from dynamic data

K. P. Unnikrishnan, Naren Ramakrishnan, P. S. Sastry, Ramasamy Uthurusamy
2006 SIGKDD Explorations  
The fourth SIGKDD workshop on temporal data mining focused on the question: What can we infer about the structure of a complex dynamical system from observed temporal data?  ...  Over the past decade, many powerful data mining techniques have been developed to analyze temporal and sequential data.  ...  by combining discovery of different types of episodes with suitable temporal constraints, one can discover the network structures and connectivity patterns of the neurons constituting the network.  ... 
doi:10.1145/1233321.1233335 fatcat:n6fkehevtvg4hfjwkcmadb7dee

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

Sunghee Oh, Seongho Song, Gregory Grabowski, Hongyu Zhao, James P. Noonan
2013 BioMed Research International  
We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.  ...  However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems.  ...  Acknowledgments This work was supported in part by NIH GM094780 (J. P. Noonan), GM59507 (H. Zhao) and NSF DMS 1106738 (H. Zhao).  ... 
doi:10.1155/2013/203681 pmid:23586021 pmcid:PMC3622290 fatcat:bojm6ntuwrg2vdzhz5dicq6rla

Gene network analysis: from heart development to cardiac therapy

Riccardo Bellazzi, Felix Engel, Fulvia Ferrazzi
2015 Thrombosis and Haemostasis  
Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies.  ...  In this review we focus on the use of gene network analysis in the study of heart development.  ...  Networks can be derived by combining data and knowledge already collected in biological data repositories.  ... 
doi:10.1160/th14-06-0483 pmid:25231088 fatcat:wj4acrws5jcibkhxt4zojwv7fy

Towards Knowledge Discovery from cDNA Microarray Gene Expression Data [chapter]

Jan Komorowski, Torgeir R. Hvidsten, Tor-Kristian Jenssen, Dyre Tjeldvoll, Eivind Hovig, Arne K. Sandvik, Astrid Lægreid
2000 Lecture Notes in Computer Science  
the richness of the microarray data.  ...  Initial experiments suggest that genes with similar function have similar expression patterns in microarray experiments.  ...  The tools are now in use in our project on developing genomic classifiers from microarray data and background knowledge.  ... 
doi:10.1007/3-540-45372-5_53 fatcat:7mpaomislrgvhbfpjep4dhzrym

On-Chip Living-Cell Microarrays for Network Biology [chapter]

Ronnie Willaert, Hichem Sahli
2011 Bioinformatics - Trends and Methodologies  
Work scheme for on-chip cellular microarray screening and biological network analysis. Summary In this chapter, on-chip living-cell microarrays to study network biology is reviewed.  ...  Integration of dynamic localisomics data with other avialable biological network data allows performing a quantitative system-wide analysis for a particular cell.  ...  This book suits young researchers who seek basic fundamentals of bioinformatic skills such as data mining, data integration, sequence analysis and gene expression analysis as well as scientists who are  ... 
doi:10.5772/20499 fatcat:vscqipttvbhotg2i2jyrbc6oni
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