Ting Liu, Jiamao Chen, Qian Zhang, Kyle Hippe, Cassandra Hunt, Thu Le, Renzhi Cao and Hua Tang* Pages 807 - 821 ( 15 )
Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learningbased identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.
Secretory proteins, malaria parasite, machine learning, prediction, algorithm, amino acid.
School of Basic Medical Sciences, Southwest Medical University, Luzhou, School of Basic Medical Sciences, Southwest Medical University, Luzhou, School of Basic Medical Sciences, Southwest Medical University, Luzhou, Department of Computer Science, Pacific Lutheran University, Department of Computer Science, Pacific Lutheran University, Department of Computer Science, Pacific Lutheran University, Department of Computer Science, Pacific Lutheran University, School of Basic Medical Sciences, Southwest Medical University, Luzhou