Hiroshi Mamitsuka works mostly in the field of Semantic similarity, limiting it down to concerns involving Information retrieval and, occasionally, Search engine indexing. He performs multidisciplinary studies into Search engine indexing and Information retrieval in his work. His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition (psychology). As part of his studies on Pattern recognition (psychology), Hiroshi Mamitsuka frequently links adjacent subjects like Artificial intelligence. With his scientific publications, his incorporates both Computational biology and Gene. His multidisciplinary approach integrates Gene and Computational biology in his work. He performs multidisciplinary study in Data mining and Feature selection in his work. By researching both Feature selection and Data mining, he produces research that crosses academic boundaries. His research is interdisciplinary, bridging the disciplines of Ranking (information retrieval) and Machine learning.
Hiroshi Mamitsuka conducts interdisciplinary study in the fields of Artificial intelligence and Information retrieval through his works. His study on Machine learning is mostly dedicated to connecting different topics, such as Markov chain. Markov chain is closely attributed to Machine learning in his work. Hiroshi Mamitsuka integrates several fields in his works, including Computational biology and Bioinformatics. Hiroshi Mamitsuka merges Bioinformatics with Computational biology in his research. He undertakes multidisciplinary studies into Data mining and Cluster analysis in his work. He performs integrative study on Cluster analysis and Data mining. Genetics is closely attributed to Gene expression in his study. Gene expression connects with themes related to Genetics in his study.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Query Learning Strategies Using Boosting and Bagging
Naoki Abe;Hiroshi Mamitsuka.
international conference on machine learning (1998)
Similarity-based machine learning methods for predicting drug–target interactions: a brief review
Hao Ding;Ichigaku Takigawa;Hiroshi Mamitsuka;Shanfeng Zhu.
Briefings in Bioinformatics (2014)
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions
Xiaodong Zheng;Hao Ding;Hiroshi Mamitsuka;Shanfeng Zhu.
knowledge discovery and data mining (2013)
Predicting Peptides That Bind to MHC Molecules Using Supervised Learning of Hidden Markov Models
Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools
Lianming Zhang;Keiko Udaka;Hiroshi Mamitsuka;Shanfeng Zhu.
Briefings in Bioinformatics (2012)
Selecting features in microarray classification using ROC curves
Pattern Recognition (2006)
A spectral clustering approach to optimally combining numericalvectors with a modular network
Motoki Shiga;Ichigaku Takigawa;Hiroshi Mamitsuka.
knowledge discovery and data mining (2007)
Multiple Graph Label Propagation by Sparse Integration
Masayuki Karasuyama;Hiroshi Mamitsuka.
IEEE Transactions on Neural Networks (2013)
A probabilistic model for mining implicit ‘chemical compound–gene’ relations from literature
Shanfeng Zhu;Yasushi Okuno;Gozoh Tsujimoto;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.
Nucleic Acids Research (2004)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: