Skip to main content
padlock icon - secure page this page is secure

QSAR and Complex Network Recognition of miRNAs in Stem Cells

Buy Article:

$68.00 + tax (Refund Policy)

Quantitative structure–activity relationship (QSAR) models have application in bioorganic chemistry mainly to the study of small sized molecules while applications to biopolymers remain not very developed. MicroRNAs (miRNAs), which are non-coding small RNAs, regulate a variety of biological processes and constitute good candidates to scale up the application of QSAR and complex network (CN). In this work, we selected microRNAs and predicted activity profile subsequently represented as a large network, which may be used to identify stem cell microRNAs with similar action. The propensity of a small RNA sequence to act as miRNA depends on its secondary structure, which one can explain in terms of folding thermodynamic and topological parameters; these can be used for fast identification of miRNAs at early stages of development of stem cells, and gain clarity inside cellular differentiation processes and diseases such as cancer. First, we calculated thermodynamic parameters and topological descriptors for 432 small RNA sequences. The model correctly recognized 203 of smiRNAs (94.0 %) and 216 of non-smiRNAs (100.0 %) divided into both training and validation series used to extend model validation for network construction. ROC curve analysis (area = 0.99) demonstrated that the present model significantly differentiates from a random classifier. In addition, a double ordinate cartesian plot of cross-validated residuals, standard residuals and leverages defined the domain of applicability of the model as a squared area within ±2 band for residuals and a leverage threshold of h = 0.0466. Last, we accounted for the methodology to combine QSAR and CN to carry out a study that would allow us to differentiate the activity of smiRNAs. The network predicted has 216 nodes (smiRNAs), 1948 edges (pairs of smiRNAs with similar activity), and low coverage density d = 8.4%. Comparative studies with real networks reveal that our network apparently has not only an ideal behavior but also resembles the known network models in different aspects. The combination of QSAR and CN is used for quickly accurate selection of new smiRNAs with potential use in bioorganic and medicinal chemistry.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: 1D and 2D descriptors; Cancer; OD; QSAR; RNA structure; complex network; miRNA prediction; spectral moments; stem cells; thermodynamic parameters; topological descriptor

Document Type: Research Article

Publication date: September 1, 2013

More about this publication?
  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more