J Zahiri, B Khorsand-Ghaffari, RSH Zade, M Kargar… – bioRxiv, 2017

Authors
Javad Zahiri, Babak Khorsand-Ghaffari, Ramin Shirali Hossein Zade, Mohammadjavad Kargar, Ali Akbar Yousefi
Publication date
2017/1/1
Journal
bioRxiv
Pages
233601
Publisher
Cold Spring Harbor Laboratory
Description
Angiogenesis inhibition research is a cutting edge in angiogenesis-dependent disease therapy, and especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the cancer treatment field. In the current study we propose 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 17000 features have been extracted to encode the peptides. However, finally, more than 2000 informative features have been selected to train the classifiers. According to the obtained results AntAngioCOOL can effectively predict anti-angiogenic peptides: this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/.

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