Literature DB >> 34636289

The Development of Machine Learning Methods in Discriminating Secretory Proteins of Malaria Parasite.

Ting Liu1, Jiamao Chen1, Qian Zhang1, Kyle Hippe2, Cassandra Hunt2, Thu Le2, Renzhi Cao2, Hua Tang3,4.   

Abstract

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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

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Keywords:  Secretory proteins; algorithm; amino acid; machine learning; malaria parasite; prediction

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Year:  2022        PMID: 34636289     DOI: 10.2174/0929867328666211005140625

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.530


  1 in total

1.  Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification.

Authors:  Ashit Kumar Dutta; R Uma Mageswari; A Gayathri; J Mary Dallfin Bruxella; Mohamad Khairi Ishak; Samih M Mostafa; Habib Hamam
Journal:  Comput Intell Neurosci       Date:  2022-06-01
  1 in total

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