Zahiri, J. | Search bioRxiv

bioRxiv2017

AntAngioCOOL: An R Package for Computational Detection of Anti-Angiogenic Peptides

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/.

bioinformatics↗

bioRxiv2017

afpCOOL: An Accurate Tool for Antifreeze Protein Detection

Various cold-adapted organisms produce antifreeze proteins (AFPs), which prevent to freeze of cell fluids by resisting the growth of the ice crystal. AFPs are currently being recognized in various organisms that are living in extremely low temperatures. AFPs have several important applications in increasing freeze tolerance of plants; maintain the tissue in frozen conditions and producing cold-hardy plants using transgenic technology. Substantial differences in the sequence and structure of the AFPs, pose a challenge for researcher to identify these proteins. In this paper, we proposed a novel method for identifying AFPs using support vector machine (SVM) by incorporating 4 types of features. Results on two benchmark datasets revealed the strength of the proposed method in AFP prediction. Also, according to the results on an independent test set, our method outperformed the current state-of-the-art methods. The further analysis showed the non-satisfactory performance of the BLAST in AFP detection: more than 62% of the BLAST searches have specificity less than 10% and there is no any BLAST search with sensitivity higher than 10%. These results reveal the urgent need for an accurate tool for AFP detection. In addition, the comparison results of the discrimination power of different feature types disclosed that evolutionary features and amino acid composition are the most contributing features in AFP detection. This method has been implemented as a stand-alone tool, namely afpCOOL, for various operating systems to predict AFPs with a user friendly graphical interface.\n\nAvailabilityafpCOOL is freely available at http://bioinf.modares.ac.ir:8080/AFPCOOL/page/afpcool.isp\n\nContactDr Zahiri zahiri@modares.ac.ir

bioinformatics↗