MLACP: Machine-learning-based prediction of anticancer peptides

Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, Sun Choi, Myeong Ok Kim, Gwang Lee

Research output: Contribution to journalArticlepeer-review

140 Scopus citations

Abstract

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab. org/MLACP.html.

Original languageEnglish
Pages (from-to)77121-77136
Number of pages16
JournalOncotarget
Volume8
Issue number44
DOIs
StatePublished - 2017

Keywords

  • Anticancer peptides
  • Hybrid model
  • Machine-learning parameters
  • Random forest
  • Support vector machine

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