TY - JOUR
T1 - Bioinformatics approaches for unveiling virus-host interactions
AU - Iuchi, Hitoshi
AU - Kawasaki, Junna
AU - Kubo, Kento
AU - Fukunaga, Tsukasa
AU - Hokao, Koki
AU - Yokoyama, Gentaro
AU - Ichinose, Akiko
AU - Suga, Kanta
AU - Hamada, Michiaki
N1 - Funding Information:
We would like to express our appreciation to Atsushi Takeda, Waseda University for his valuable and constructive suggestions. This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI [grant Nos.: JP21K15078 to HI, JP22J00010 to JK]. Waseda Research Institute for Science and Engineering, Grant-in-Aid for Young Scientists (Early Bird) [to JK], and AMED [Grant Nos.: JP21fk0108104 and JP22ama121055 to MH].
Funding Information:
We would like to express our appreciation to Atsushi Takeda, Waseda University for his valuable and constructive suggestions. This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI [grant Nos.: JP21K15078 to HI, JP22J00010 to JK]. Waseda Research Institute for Science and Engineering , Grant-in-Aid for Young Scientists (Early Bird) [to JK], and AMED [Grant Nos.: JP21fk0108104 and JP22ama121055 to MH].
Publisher Copyright:
© 2023 The Authors
PY - 2023/1
Y1 - 2023/1
N2 - The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus–host interactions through host range prediction and protein–protein interaction prediction. Although many algorithms have been developed to predict virus–host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus–host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus–host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.
AB - The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus–host interactions through host range prediction and protein–protein interaction prediction. Although many algorithms have been developed to predict virus–host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus–host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus–host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.
KW - Host range prediction
KW - Protein–protein interaction prediction
KW - Virus–host interaction
UR - http://www.scopus.com/inward/record.url?scp=85149172378&partnerID=8YFLogxK
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U2 - 10.1016/j.csbj.2023.02.044
DO - 10.1016/j.csbj.2023.02.044
M3 - Review article
AN - SCOPUS:85149172378
SN - 2001-0370
VL - 21
SP - 1774
EP - 1784
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -