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Hiroshi Mamitsuka

Hiroshi Mamitsuka

D-Index & Metrics

Computer Science

D-Index
41
Citations
7026
World Ranking
8849
National Ranking
123

Overview

Hiroshi Mamitsuka is affiliated with Kyoto University in Japan and has a substantial publication record in the intersection of biochemistry, genetics, molecular biology, and computer science. Their research predominantly spans molecular biology and computational theory, with a notable emphasis on artificial intelligence and its applications.

Their recent scholarly output includes several papers published between 2020 and 2022. Among these are "DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction" (2021, Bioinformatics), "Machine learning approaches for drug combination therapies" (2021, Briefings in Bioinformatics), "Eukaryotic virus composition can predict the efficiency of carbon export in the global ocean" (2020, iScience), "BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text" (2020, Bioinformatics), and "DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction" (2022, Bioinformatics).

The primary topics covered in their research include computational drug discovery methods, bioinformatics and genomic networks, biomedical text mining and ontologies, machine learning in bioinformatics, vaccines and immunoinformatics approaches, machine learning in materials science, and advanced graph neural networks.

The scientist regularly collaborates with several coauthors. Frequent collaborators include Canh Hao Nguyen, Shanfeng Zhu, Ronghui You, Hai Nguyen, and Đức Anh Nguyễn.

Hiroshi Mamitsuka's work has appeared in various publication venues, with multiple contributions to:

  • Bioinformatics
  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Briefings in Bioinformatics
  • Machine Learning

Their scientific pursuits demonstrate a blend of expertise in computational methods and their applications to biological systems, focusing on both theoretical and practical aspects of machine learning-enhanced biological data analysis.

Best Publications

  • Query Learning Strategies Using Boosting and Bagging

    Naoki Abe;Hiroshi Mamitsuka

  • Similarity-based machine learning methods for predicting drug–target interactions: a brief review

    Hao Ding;Ichigaku Takigawa;Hiroshi Mamitsuka;Shanfeng Zhu

  • Collaborative matrix factorization with multiple similarities for predicting drug-target interactions

    Xiaodong Zheng;Hao Ding;Hiroshi Mamitsuka;Shanfeng Zhu

  • Predicting Peptides That Bind to MHC Molecules Using Supervised Learning of Hidden Markov Models

    Hiroshi Mamitsuka

  • GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank

    Ronghui You;Zihan Zhang;Yi Xiong;Fengzhu Sun;Fengzhu Sun

  • AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

    Ronghui You;Zihan Zhang;Ziye Wang;Suyang Dai

  • Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools

    Lianming Zhang;Keiko Udaka;Hiroshi Mamitsuka;Shanfeng Zhu

  • NetGO: improving large-scale protein function prediction with massive network information

    Ronghui You;Ronghui You;Shuwei Yao;Shuwei Yao;Yi Xiong;Xiaodi Huang

  • Multiple Graph Label Propagation by Sparse Integration

    Masayuki Karasuyama;Hiroshi Mamitsuka

  • DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

    Qingjun Yuan;Junning Gao;Dongliang Wu;Shihua Zhang

  • Selecting features in microarray classification using ROC curves

    Hiroshi Mamitsuka

  • A probabilistic model for mining implicit ‘chemical compound–gene’ relations from literature

    Shanfeng Zhu;Yasushi Okuno;Gozoh Tsujimoto;Hiroshi Mamitsuka

  • DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction.

    Ronghui You;Shuwei Yao;Hiroshi Mamitsuka;Hiroshi Mamitsuka;Shanfeng Zhu

  • DeepMeSH: deep semantic representation for improving large-scale MeSH indexing

    Shengwen Peng;Ronghui You;Hongning Wang;Chengxiang Zhai

  • A spectral clustering approach to optimally combining numericalvectors with a modular network

    Motoki Shiga;Ichigaku Takigawa;Hiroshi Mamitsuka

  • Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity

    Shanfeng Zhu;Jia Zeng;Hiroshi Mamitsuka

  • KCaM (KEGG Carbohydrate Matcher): a software tool for analyzing the structures of carbohydrate sugar chains

    Kiyoko F. Aoki;Atsuko Yamaguchi;Nobuhisa Ueda;Tatsuya Akutsu

  • Calpain Cleavage Prediction Using Multiple Kernel Learning

    David A. duVerle;Yasuko Ono;Hiroyuki Sorimachi;Hiroshi Mamitsuka

  • Machine learning approaches for drug combination therapies.

    Betül Güvenç Paltun;Betül Güvenç Paltun;Samuel Kaski;Samuel Kaski;Samuel Kaski;Hiroshi Mamitsuka;Hiroshi Mamitsuka;Hiroshi Mamitsuka

  • Predicting Protein Secondary Structure Using Stochastic Tree Grammars

    Naoki Abe;Hiroshi Mamitsuka

  • Efficient tree-matching methods for accurate carbohydrate database queries.

    Kiyoko F. Aoki;Atsuko Yamaguchi;Yasushi Okuno;Tatsuya Akutsu

Frequent Co-Authors

Minoru Kanehisa
Minoru Kanehisa Kyoto University
Samuel Kaski
Samuel Kaski Aalto University
Naoki Abe
Naoki Abe IBM (United States)
Tatsuya Akutsu
Tatsuya Akutsu Kyoto University
Fengzhu Sun
Fengzhu Sun University of Southern California
Koji Tsuda
Koji Tsuda University of Tokyo
Kiyoko F. Aoki-Kinoshita
Kiyoko F. Aoki-Kinoshita Soka University of America
Gozoh Tsujimoto
Gozoh Tsujimoto Kyoto University
Hiroyuki Sorimachi
Hiroyuki Sorimachi Tokyo Metropolitan Institute of Medical Science
Lionel Guidi
Lionel Guidi Centre national de la recherche scientifique, CNRS

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