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Yoshihiro Yamanishi
Kyushu Institute of Technology
90Publications
26H-index
5,486Citations
Publications 90
Newest
#1Francois Berenger (Kyushu Institute of Technology)
#2Kam Y. J. ZhangH-Index: 27
Last.Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 26
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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.
#1Sayaka Akiyoshi (Kyushu University)H-Index: 1
#2Michio Iwata (Kyushu Institute of Technology)H-Index: 5
Last.Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 26
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#1Yasuo TabeiH-Index: 14
#2Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 26
Last.Rasmus Pagh (ITU: IT University of Copenhagen)H-Index: 27
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String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approxim...
#1Michio Iwata (Kyushu Institute of Technology)H-Index: 5
#2Longhao Yuan (Saitama Institute of Technology)
Last.Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 26
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#1Yasuo TabeiH-Index: 14
#2Masaaki Kotera (UTokyo: University of Tokyo)H-Index: 2
Last.Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 26
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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...
#1Francois Berenger (Kyushu Institute of Technology)
#2Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 26
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...
#1Yoshihiro Yamanishi (Kyushu Institute of Technology)H-Index: 26
#2Yasubumi Sakakibara (Keio: Keio University)H-Index: 29
Last.Yangjun Chen (La Trobe University)H-Index: 27
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#1Takashi Kamada (Kurume University)
#2Shiroh Miura (Kurume University)H-Index: 8
Last.Takayuki Taniwaki (Kurume University)H-Index: 21
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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...
#1Ryusuke Sawada (Kyushu University)H-Index: 7
#2Michio Iwata (Kyushu University)H-Index: 5
Last.Yoshihiro Yamanishi (Kyushu University)H-Index: 26
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Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds. Specifically, we integrated chemically-induced and genetically-perturbed gene expression profiles in ...
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