دورية أكاديمية
أعرض في EDS

Identifying cancer specific functionally relevant miRNAs from gene expression and miRNA-to-gene networks using regularized regression.

التفاصيل البيبلوغرافية
العنوان: Identifying cancer specific functionally relevant miRNAs from gene expression and miRNA-to-gene networks using regularized regression.
المؤلفون: Mezlini AM; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada ; Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario, Canada., Wang B, Deshwar A, Morris Q, Goldenberg A
المصدر: PloS one [PLoS One] 2013 Oct 02; Vol. 8 (10), pp. e73168. Date of Electronic Publication: 2013 Oct 02 (Print Publication: 2013).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Gene Expression Profiling* , Gene Regulatory Networks*, Computational Biology/*methods , Glioblastoma/*genetics , MicroRNAs/*genetics , Prostatic Neoplasms/*genetics, Humans ; Linear Models ; Male
مستخلص: Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNA's differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network. Our method is based on a linear regression with an elastic net regularization. Our simulations show that, with our method, the active miRNAs can be detected with high accuracy and our approach is robust to high levels of noise and missing information. Furthermore, our results on real datasets for glioblastoma and prostate cancer are confirmed by microRNA expression measurements. Our method leads to the selection of potentially functionally important microRNAs. The associations of some of our identified miRNAs with cancer mechanisms are already confirmed in other studies (hypoxia related hsa-mir-210 and apoptosis-related hsa-mir-296-5p). We have also identified additional miRNAs that were not previously studied in the context of cancer but are coherently predicted as active by our method and may warrant further investigation. The code is available in Matlab and R and can be downloaded on http://www.cs.toronto.edu/goldenberg/Anna_Goldenberg/Current_Research.html.
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المشرفين على المادة: 0 (MicroRNAs)
تواريخ الأحداث: Date Created: 20131008 Date Completed: 20140623 Latest Revision: 20231110
رمز التحديث: 20231110
مُعرف محوري في PubMed: PMC3788788
DOI: 10.1371/journal.pone.0073168
PMID: 24098326
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0073168