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Yoshihiro Yamanishi
Kyushu Institute of Technology
Machine learningData miningComputer scienceBioinformaticsBiology
91Publications
27H-index
6,000Citations
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Publications 96
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In Ligand-Based Virtual Screening, High-Throughput Screening (HTS) datasets can be exploited to train classification models. Such models can be used to prioritize yet untested molecules, from the most likely active (against a protein target of interest) to the least likely active. In this study, a single-parameter ranking method with an Applicability Domain (AD) is proposed. In effect, Kernel Density Estimates (KDE) are revisited to improve their computational efficiency and incorporate an AD. T...
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#1Shonosuke Harada (Kyoto University)H-Index: 1
#2Hirotaka AkitaH-Index: 1
Last. Hisashi Kashima (Kyoto University)H-Index: 28
view all 7 authors...
Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches inclu...
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#1Itsuki Fukunaga (Kyushu Institute of Technology)
#2Ryusuke Sawada (Kyushu Institute of Technology)H-Index: 7
Last. Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 27
view all 6 authors...
Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In this study, we propose a novel deep learning-based method to predict the health effects of food peptides and elucidate the mode-of-action. In the algorithm, we estimate potential target proteins of food peptides using a multi-task graph convolutional ...
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#1Sayaka Akiyoshi (Kyushu University)H-Index: 2
#2Michio Iwata (Kyushu Institute of Technology)H-Index: 5
Last. Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 27
view all 4 authors...
Glycans play important roles in cell communication, protein interaction, and immunity, and structural changes in glycans are associated with the regulation of a range of biological pathways involved in disease. However, our understanding of the detailed relationships between specific diseases and glycans is very limited. In this study, we proposed an omics-based method to investigate the correlations between glycans and a wide range of human diseases. We analyzed the gene expression patterns of ...
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#1Yuto Amano (Kao Corporation)
#2Hiroshi Honda (Kao Corporation)H-Index: 8
Last. Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 27
view all 8 authors...
In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms wit...
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#1Michio Iwata (Kyushu Institute of Technology)H-Index: 5
#2Longhao Yuan (Saitama Institute of Technology)
Last. Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 27
view all 9 authors...
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#1Yasuo TabeiH-Index: 14
#2Masaaki Kotera (UTokyo: University of Tokyo)H-Index: 18
Last. Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 27
view all 4 authors...
Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology. We present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call “drug-protein interaction signatures” from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extr...
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#1Francois Berenger (Kyushu Institute of Technology)H-Index: 1
#2Kam Y. J. ZhangH-Index: 16
Last. Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 27
view all 3 authors...
Background OCaml is a functional programming language with strong static types, Hindley–Milner type inference and garbage collection. In this article, we share our experience in prototyping chemoinformatics and structural bioinformatics software in OCaml.
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#1Francois Berenger (Kyushu Institute of Technology)H-Index: 1
#2Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 27
In Quantitative Structure–Activity Relationship (QSAR) modeling, one must come up with an activity model but also with an applicability domain for that model. Some existing methods to create an applicability domain are complex, hard to implement, and/or difficult to interpret. Also, they often require the user to select a threshold value, or they embed an empirical constant. In this work, we propose a trivial to interpret and fully automatic Distance-Based Boolean Applicability Domain (DBBAD) al...
3 CitationsSource
#1Takashi Kamada (Kurume University)
#2Shiroh Miura (Kurume University)H-Index: 8
Last. Takayuki Taniwaki (Kurume University)H-Index: 23
view all 7 authors...
Abstract Background and objectives Meta-iodobenzylguanidine (MIBG) myocardial scintigraphy is an effective tool for distinguishing Parkinson's disease (PD) from other diseases accompanied by parkinsonism. Unlike other Parkinsonian diseases, in PD, MIBG accumulation in the heart tends to decrease. However, previous studies have reported that a decrease in MIBG accumulation also occurs in progressive supranuclear palsy (PSP). Thus, we analyzed the relationship between the degree of MIBG accumulati...
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