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Kengo Kinoshita

Researcher at Tohoku University

Publications -  217
Citations -  8041

Kengo Kinoshita is an academic researcher from Tohoku University. The author has contributed to research in topics: Population & Gene. The author has an hindex of 42, co-authored 189 publications receiving 6644 citations. Previous affiliations of Kengo Kinoshita include Yokohama City University & University of Tokyo.

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PrDOS: prediction of disordered protein regions from amino acid sequence

TL;DR: PrDOS is a server that predicts the disordered regions of a protein from its amino acid sequence and returns a two-state prediction (order/disorder) and a disorder probability for each residue.
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ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis

TL;DR: An Arabidopsis thaliana trans-factor and cis-element prediction database that provides co-regulated gene relationships based on co-expressed genes deduced from microarray data and the predicted cis elements to help researchers to clarify the function and regulation of particular genes and gene networks is reported.
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ATTED-II provides coexpressed gene networks for Arabidopsis.

TL;DR: ATTED-II as mentioned in this paper is a database of gene coexpression in Arabidopsis that can be used to design a wide variety of experiments, including the prioritization of genes for functional identification or for studies of regulatory relationships.
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Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals

TL;DR: The value of high-coverage sequencing for constructing population-specific variant panels, which covers 99.0% SNVs of minor allele frequency ≥0.1%, is demonstrated, and its value for identifying causal rare variants of complex human disease phenotypes in genetic association studies is demonstrated.
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Prediction of disordered regions in proteins based on the meta approach

TL;DR: A new prediction method for disordered regions in proteins based on the meta approach is developed and a web-server is implemented for this prediction method named 'metaPrDOS', which achieved higher prediction accuracy than all methods participating in CASP7.