DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

Journal: Bioinformatics (Oxford, England)
PMID:

Abstract

MOTIVATION: Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted.

Authors

  • Matee Ullah
    Nanjing University of Science and Technology, China.
  • Shahid Akbar
    Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
  • Ali Raza
    Department of Medical Microbiology and Clinical Microbiology, Near East University, Cyprus.
  • Quan Zou