Machine learning enhances prediction of plants as potential sources of antimalarials

• Published in: Frontiers in plant science Vol. 14; p. 1173328
• Main Authors: Richard-Bollans, Adam, Aitken, Conal, Antonelli, Alexandre, Bitencourt, Cássia, Goyder, David, Lucas, Eve, Ondo, Ian, Pérez-Escobar, Oscar A., Pironon, Samuel, Richardson, James E., Russell, David, Silvestro, Daniele, Wright, Colin W., Howes, Melanie-Jayne R.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 2023
• Subjects: antiplasmodial activity; Biologi; Biological Sciences; botany; ethnobotany; ethnopharmacology; machine learning; malaria; Plant Science; sampling bias; traditional and indigenous knowledge; traditional and indigenous knowledge; antiplasmodial activity; botany; sampling bias; machine learning; malaria; ethnobotany; ethnopharmacology

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-con...