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Satoru Miyano

Researcher at Tokyo Medical and Dental University

Publications -  874
Citations -  45801

Satoru Miyano is an academic researcher from Tokyo Medical and Dental University. The author has contributed to research in topics: Gene & Gene regulatory network. The author has an hindex of 84, co-authored 811 publications receiving 38723 citations. Previous affiliations of Satoru Miyano include University of Paderborn & Institute of Medical Science.

Papers
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Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

TL;DR: A new statistical approach is proposed that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions and can identifyChanges on regulations more accurately than existing methods.
Journal ArticleDOI

Paraneoplastic hypereosinophilic syndrome associated with IL3‐IgH positive acute lymphoblastic leukemia

TL;DR: An 11-year-old male with ALL with hypereosinophilia was encountered and three-drug therapy with prednisolone, vincristine, and Lasparaginase was successful with an induction of morphological remission, and headto-head gene fusion between the immunoglobulin heavy-chain joining enhancer and IL3 promoter region was confirmed.
Patent

Estimating gene networks using inferential methods and biological constraints

Sascha Ott, +1 more
TL;DR: In this article, a general approach to reduce the search space to a biologically meaningful subspace and to find optimal solutions within the subspace in linear time by using inferential models constrained by biologically relevant information is presented.
Journal ArticleDOI

ExonMiner: Web service for analysis of GeneChip Exon array data

TL;DR: ExonMiner is the first all-in-one web service for analysis of exon array data to detect transcripts that have significantly different splicing patterns in two cells, e.g. normal and cancer cells, and has the potential to reveal the aberrant splice variations in cancer cells as exon level biomarkers.