AntAngioCOOL: computational detection of anti-angiogenic peptides

Zahiri, J ; Sharif University of Technology | 2019

577 Viewed
  1. Type of Document: Article
  2. DOI: 10.1186/s12967-019-1813-7
  3. Publisher: BioMed Central Ltd , 2019
  4. Abstract:
  5. Background: Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment. Methods: A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study. Also, 20% of each class were selected to construct an independent test dataset (see Additional files 1, 2). We proposed an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17,000 features were extracted to encode the peptides. Results: Finally, more than 2000 informative features were selected to train the classifiers for detecting anti-angiogenic peptides. AntAngioCOOL includes three different models that can be selected by the user for different purposes; it is the most sensitive, most specific and most accurate. According to the obtained results AntAngioCOOL can effectively suggest anti-angiogenic peptides; this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on the independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/. Conclusions: Only 2% of the extracted descriptors were used to build the predictor models. The results revealed that physico-chemical profile is the most important feature type in predicting anti-angiogenic peptides. Also, atomic profile and PseAAC are the other important features. © 2019 The Author(s)
  6. Keywords:
  7. Angiogenesis ; Anti-angiogenic ; Cancer ; Cancer treatment ; Machine learning ; Peptide ; Anti angiogenic peptide ; Unclassified drug ; Angiogenesis inhibitor ; Angiogenic protein ; Amino acid composition ; Cancer therapy ; Classifier ; Controlled study ; Measurement accuracy ; Physical chemistry ; Prediction ; Protein analysis ; Sensitivity and specificity ; Biology ; Software ; Angiogenesis Inhibitors ; Angiogenic Proteins ; Computational Biology
  8. Source: Journal of Translational Medicine ; Volume 17, Issue 1 , 2019 ; 14795876 (ISSN)
  9. URL: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-019-1813-7