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

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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.

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:29

Enthalten in:

Current medicinal chemistry - 29(2022), 5 vom: 12., Seite 807-821

Sprache:

Englisch

Beteiligte Personen:

Liu, Ting [VerfasserIn]
Chen, Jiamao [VerfasserIn]
Zhang, Qian [VerfasserIn]
Hippe, Kyle [VerfasserIn]
Hunt, Cassandra [VerfasserIn]
Le, Thu [VerfasserIn]
Cao, Renzhi [VerfasserIn]
Tang, Hua [VerfasserIn]

Links:

Volltext

Themen:

Algorithm
Amino acid
Journal Article
Machine learning
Malaria parasite
Prediction
Protozoan Proteins
Secretory proteins

Anmerkungen:

Date Completed 02.03.2022

Date Revised 31.05.2022

published: Print

Citation Status MEDLINE

doi:

10.2174/0929867328666211005140625

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM331771535