Hiroshi Mamitsuka
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Explore the profile of Hiroshi Mamitsuka including associated specialties, affiliations and a list of published articles.
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112
Citations
1273
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Recent Articles
1.
Yan H, Wang S, Liu H, Mamitsuka H, Zhu S
Bioinformatics
. 2024 Sep;
40(Suppl 2):ii53-ii61.
PMID: 39230707
Summary: The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approaches focus on...
2.
Yoshikawa C, Nguyen D, Nakaji-Hirabayashi T, Takigawa I, Mamitsuka H
ACS Biomater Sci Eng
. 2024 Mar;
10(4):2165-2176.
PMID: 38546298
Manipulating the three-dimensional (3D) structures of cells is important for facilitating to repair or regenerate tissues. A self-assembly system of cells with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs)...
3.
Qu W, You R, Mamitsuka H, Zhu S
Bioinformatics
. 2023 Sep;
39(9).
PMID: 37669154
Motivation: Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized...
4.
Nguyen D, Nguyen C, Mamitsuka H
IEEE Trans Neural Netw Learn Syst
. 2023 Apr;
35(8):11620-11625.
PMID: 37018091
Predicting drug-drug interactions (DDIs) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem...
5.
Liao Z, Xie L, Mamitsuka H, Zhu S
Bioinformatics
. 2022 Dec;
39(1).
PMID: 36576008
Motivation: Finding molecules with desired pharmaceutical properties is crucial in drug discovery. Generative models can be an efficient tool to find desired molecules through the distribution learned by the model...
6.
Nguyen D, Nguyen C, Petschner P, Mamitsuka H
Bioinformatics
. 2022 Jun;
38(Suppl 1):i333-i341.
PMID: 35758803
Motivation: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs...
7.
You R, Qu W, Mamitsuka H, Zhu S
Bioinformatics
. 2022 Jun;
38(Suppl 1):i220-i228.
PMID: 35758790
Motivation: Computationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance...
8.
Hiremath S, Wittke S, Palosuo T, Kaivosoja J, Tao F, Proll M, et al.
PLoS One
. 2021 Dec;
16(12):e0251952.
PMID: 34914721
Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop...
9.
Liu L, Mamitsuka H, Zhu S
Bioinformatics
. 2021 Oct;
38(3):799-808.
PMID: 34672333
Motivation: Deciphering the relationship between human genes/proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized...
10.
Guvenc Paltun B, Kaski S, Mamitsuka H
Brief Bioinform
. 2021 Aug;
22(6).
PMID: 34368832
Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited...