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Yoshihiro Yamanishi

Yoshihiro Yamanishi

D-Index & Metrics

Computer Science

D-Index
38
Citations
14228
World Ranking
9953
National Ranking
147

Overview

Yoshihiro Yamanishi is affiliated with Nagoya University in Japan. Their research predominantly spans the fields of Biochemistry, Genetics and Molecular Biology as well as Computer Science.

Their work focuses on a variety of specialized subfields including Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Pharmacology, and Oncology.

The main topics covered in their research involve Computational Drug Discovery Methods, Machine Learning in Materials Science, Bioinformatics and Genomic Networks, Protein Structure and Dynamics, vaccines and immunoinformatics approaches, Metabolomics and Mass Spectrometry Studies, and Microbial Natural Products and Biosynthesis.

Yamanishi has contributed frequently to a number of publication venues. These include:

  • Journal of Chemical Information and Modeling
  • Bioinformatics
  • Molecular Informatics
  • BMC Bioinformatics
  • iScience

Some of their recent papers are:

  • Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking, 2021, Journal of Chemical Information and Modeling
  • The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer, 2021, Cancer Science
  • Dual graph convolutional neural network for predicting chemical networks, 2020, BMC Bioinformatics
  • Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules, 2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
  • Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets, 2020, Bioinformatics

Their frequent coauthors include:

  • M Iwata
  • Ryusuke Sawada
  • Chen Li
  • Tomokazu Shibata
  • Momoko Hamano

Best Publications

  • KEGG for linking genomes to life and the environment

    Minoru Kanehisa;Michihiro Araki;Susumu Goto;Masahiro Hattori

  • Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

    Yoshihiro Yamanishi;Michihiro Araki;Alex Gutteridge;Wataru Honda

  • Supervised prediction of drug–target interactions using bipartite local models

    Kevin Bleakley;Yoshihiro Yamanishi

  • Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework

    Yoshihiro Yamanishi;Masaaki Kotera;Minoru Kanehisa;Susumu Goto

  • Protein network inference from multiple genomic data: a supervised approach

    Y. Yamanishi;J.-P. Vert;M. Kanehisa

  • Predicting drug side-effect profiles: a chemical fragment-based approach

    Edouard Pauwels;Edouard Pauwels;Edouard Pauwels;Véronique Stoven;Véronique Stoven;Véronique Stoven;Yoshihiro Yamanishi;Yoshihiro Yamanishi;Yoshihiro Yamanishi

  • Relating drug–protein interaction network with drug side effects

    Sayaka Mizutani;Edouard Pauwels;Véronique Stoven;Susumu Goto

  • Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction

    Hisashi Kashima;Tsuyoshi Kato;Yoshihiro Yamanishi;Masashi Sugiyama

  • The inference of protein–protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships

    Tetsuya Sato;Yoshihiro Yamanishi;Minoru Kanehisa;Hiroyuki Toh

  • Drug target prediction using adverse event report systems

    Masataka Takarabe;Masaaki Kotera;Yosuke Nishimura;Susumu Goto

  • Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis

    Yoshihiro Yamanishi;Jean-Philippe Vert;Akihiro Nakaya;Minoru Kanehisa

  • Drug side-effect prediction based on the integration of chemical and biological spaces.

    Yoshihiro Yamanishi;Edouard Pauwels;Edouard Pauwels;Edouard Pauwels;Masaaki Kotera

  • KEGG OC: A large-scale automatic construction of taxonomy-based ortholog clusters

    Akihiro Nakaya;Toshiaki Katayama;Masumi Itoh;Kazushi Hiranuka

  • DINIES: drug–target interaction network inference engine based on supervised analysis

    Yoshihiro Yamanishi;Masaaki Kotera;Yuki Moriya;Ryusuke Sawada

  • Supervised enzyme network inference from the integration of genomic data and chemical information

    Yoshihiro Yamanishi;Jean-Philippe Vert;Minoru Kanehisa

  • Supervised Graph Inference

    Jean-philippe Vert;Yoshihiro Yamanishi

  • Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers

    Yasuo Tabei;Edouard Pauwels;Edouard Pauwels;Edouard Pauwels;Véronique Stoven;Véronique Stoven;Véronique Stoven;Kazuhiro Takemoto;Kazuhiro Takemoto

  • E-zyme

    Yoshihiro Yamanishi;Masahiro Hattori;Masaaki Kotera;Susumu Goto

  • Extracting sets of chemical substructures and protein domains governing drug-target interactions

    Yoshihiro Yamanishi;Edouard Pauwels;Hiroto Saigo;Véronique Stoven

  • Systematic Drug Repositioning for a Wide Range of Diseases with Integrative Analyses of Phenotypic and Molecular Data

    Hiroaki Iwata;Ryusuke Sawada;Sayaka Mizutani;Yoshihiro Yamanishi

Frequent Co-Authors

Minoru Kanehisa
Minoru Kanehisa Kyoto University
Susumu Goto
Susumu Goto Osaka University
Hiroyuki Toh
Hiroyuki Toh Kwansei Gakuin University
Hisashi Kashima
Hisashi Kashima Kyoto University
Jean-Philippe Vert
Jean-Philippe Vert Google (United States)
Koji Tsuda
Koji Tsuda University of Tokyo
Rasmus Pagh
Rasmus Pagh University of Copenhagen
Simon J. Puglisi
Simon J. Puglisi University of Helsinki
Kenzaburo Tani
Kenzaburo Tani University of Tokyo
Tatsuya Akutsu
Tatsuya Akutsu Kyoto University

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