Library Subscription: Guest
Journal of Environmental Pathology, Toxicology and Oncology

Published 4 issues per year

ISSN Print: 0731-8898

ISSN Online: 2162-6537

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 2.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 2.8 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00049 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.59 SJR: 0.429 SNIP: 0.507 CiteScore™:: 3.9 H-Index: 49

Indexed in

Computational Biology of Genome Expression and Regulation—A Review of Microarray Bioinformatics

Volume 27, Issue 3, 2008, pp. 157-179
DOI: 10.1615/JEnvironPatholToxicolOncol.v27.i3.10
Get accessGet access

ABSTRACT

Microarray technology is being used widely in various biomedical research areas; the corresponding microarray data analysis is an essential step toward the best utilizing of array technologies. Here we review two components of the microarray data analysis: a low level of microarray data analysis that emphasizes the designing, the quality control, and the preprocessing of microarray experiments, then a high level of microarray data analysis that focuses on the domain-specific microarray applications such as tumor classification, biomarker prediction, analyzing array CGH experiments, and reverse engineering of gene expression networks. Additionally, we will review the recent development of building a predictive model in genome expression and regulation studies. This review may help biologists grasp a basic knowledge of microarray bioinformatics as well as its potential impact on the future evolvement of biomedical research fields.

CITED BY
  1. Deller J. R., Radha Hayder, McCormick J. Justin, Wang Huiyan, Nonlinear Dependence in the Discovery of Differentially Expressed Genes, ISRN Bioinformatics, 2012, 2012. Crossref

  2. Lancashire L. J., Lemetre C., Ball G. R., An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies, Briefings in Bioinformatics, 10, 3, 2008. Crossref

  3. Wang Junbai, Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence, BMC Bioinformatics, 15, 1, 2014. Crossref

  4. Mueller Peter P., Drynda Andreas, Goltz Diane, Hoehn René, Hauser Hansjörg, Peuster Matthias, Common signatures for gene expression in postnatal patients with patent arterial ducts and stented arteries, Cardiology in the Young, 19, 4, 2009. Crossref

  5. Lai Hung-Ming, May Sean, Mayes Sean, Pigeons: A Novel GUI Software for Analysing and Parsing High Density Heterologous Oligonucleotide Microarray Probe Level Data, Microarrays, 3, 1, 2014. Crossref

  6. Tangen Jon-Magnus, Tierens Anne, Caers Jo, Binsfeld Marilene, Olstad Ole Kristoffer, Trøseid Anne-Marie Siebke, Wang Junbai, Tjønnfjord Geir Erland, Hetland Geir, Immunomodulatory Effects of theAgaricus blazeiMurrill-Based Mushroom Extract AndoSan in Patients with Multiple Myeloma Undergoing High Dose Chemotherapy and Autologous Stem Cell Transplantation: A Randomized, Double Blinded Clinical Study, BioMed Research International, 2015, 2015. Crossref

  7. Wang Junbai, Wu Qianqian, Hu Xiaohua Tony, Tian Tianhai, An integrated approach to infer dynamic protein-gene interactions – A case study of the human P53 protein, Methods, 110, 2016. Crossref

  8. Tian Tianhai, Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data, in Translational Biomedical Informatics, 939, 2016. Crossref

  9. Martin Christian W, Tauchen Anika, Becker Anke, Nattkemper Tim W, A Normalized Tree Index for identification of correlated clinical parameters in microarray experiments, BioData Mining, 4, 1, 2011. Crossref

  10. Wang Junbai, Qianqian Wu , Tian Tianhai, Integrated study to infer dynamic protein-gene interactions in human p53 regulatory networks, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015. Crossref

  11. Wang Junbai, Computational study of associations between histone modification and protein-DNA binding in yeast genome by integrating diverse information, BMC Genomics, 12, 1, 2011. Crossref

  12. Sarigiannis Dimosthenis A., Gotti Alberto, Handakas Evangelos, Karakitsios Spyros P., Informatics and Data Analytics to Support Exposome-Based Discovery, in Applying Big Data Analytics in Bioinformatics and Medicine, 2018. Crossref

  13. Sarigiannis Dimosthenis A., Gotti Alberto, Handakas Evangelos, Karakitsios Spyros P., Informatics and Data Analytics to Support Exposome-Based Discovery, in Biotechnology, 2019. Crossref

  14. Wang Junbai, Morigen , BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors, BMC Bioinformatics, 10, 1, 2009. Crossref

  15. Wang Junbai, Davidson Ben, Tian Tianhai, Systems Biology Studies of Gene Network and Cell Signaling Pathway in Cancer Research, in Bioinformatics for Diagnosis, Prognosis and Treatment of Complex Diseases, 4, 2013. Crossref

  16. Smith Steven Christopher, Theodorescu Dan, Molecular Nomograms for Predicting Prognosis and Treatment Response, in Bladder Tumors:, 2011. Crossref

  17. Wang Junbai, Tian Tianhai, Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53, BMC Bioinformatics, 11, 1, 2010. Crossref

  18. Wu Joseph, Gupta Mayetri, Hussein Amira I., Gerstenfeld Louis, Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study, Journal of Applied Statistics, 48, 10, 2021. Crossref

  19. Pihlstrøm Hege Kampen, Ueland Thor, Michelsen Annika E., Aukrust Pål, Gatti Franscesca, Hammarström Clara, Kasprzycka Monika, Wang Junbai, Haraldsen Guttorm, Mjøen Geir, Dahle Dag Olav, Midtvedt Karsten, Eide Ivar Anders, Hartmann Anders, Holdaas Hallvard, Dor Frank JMF, Exploring the potential effect of paricalcitol on markers of inflammation in de novo renal transplant recipients, PLOS ONE, 15, 12, 2020. Crossref

  20. Pollheimer Jürgen, Bodin Johanna, Sundnes Olav, Edelmann Reidunn J., Skånland Sigrid S., Sponheim Jon, Brox Mari Johanna, Sundlisæter Eirik, Loos Tamara, Vatn Morten, Kasprzycka Monika, Wang Junbai, Küchler Axel M., Taskén Kjetil, Haraldsen Guttorm, Hol Johanna, Interleukin-33 Drives a Proinflammatory Endothelial Activation That Selectively Targets Nonquiescent Cells, Arteriosclerosis, Thrombosis, and Vascular Biology, 33, 2, 2013. Crossref

Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections Prices and Subscription Policies Begell House Contact Us Language English 中文 Русский Português German French Spain