Identification of RNA biomarkers for chemical safety screening in mouse embryonic stem cells using RNA deep sequencing analysis

Although it is not yet possible to replace in vivo animal testing completely, the need for a more efficient method for toxicity testing, such as an in vitro cell-based assay, has been widely acknowledged. Previous studies have focused on mRNAs as biomarkers; however, recent studies have revealed that non-coding RNAs (ncRNAs) are also efficient novel biomarkers for toxicity testing. Here, we used deep sequencing analysis (RNA-seq) to identify novel RNA biomarkers, including ncRNAs, that exhibited a substantial response to general chemical toxicity from nine chemicals, and to benzene toxicity specifically. The nine chemicals are listed in the Japan Pollutant Release and Transfer Register as class I designated chemical substances. We used undifferentiated mouse embryonic stem cells (mESCs) as a simplified cell-based toxicity assay. RNA-seq revealed that many mRNAs and ncRNAs responded substantially to the chemical compounds in mESCs. This finding indicates that ncRNAs can be used as novel RNA biomarkers for chemical safety screening

セイソクカホウニヨルブンプテイスウケイノドウテイニカンスルケンキュウ

京都大学0048新制・課程博士工学博士甲第2035号工博第547号新制||工||396(附属図書館)5311UT51-53-C178京都大学大学院工学研究科数理工学専攻(主査)教授 椹木 義一, 教授 得丸 英勝, 教授 明石 一学位規則第5条第1項該当Kyoto UniversityDA

Evaluating fuzzy clustering algorithms for microdata protection

Microaggregation is a well-known technique for data protection. It is usually operationally defined in a two-step process: (i) a large number of small clusters are built from data and (ii) data are replaced by cluster aggregates. In this work we study the use of fuzzy clustering in the first step. In particular, we consider standard fuzzy c-means and entropy based fuzzy c-means. For both methods, our study includes variable-size and non-variable-size variations. The resulting masking methods are compared using standard scoring methods. © Springer-Verlag 2004.Part of this research was done in a research stay of the first author at the University of Tsukuba. Work partly funded by the European Union (project ”CASC” IST-2000-25069), the MCYT (project TIC2001-4051-E) and the Generalitat de Catalunya (AGAUR, 2002XT 00111)Peer Reviewe

Hierarchical spherical clustering

This work introduces an alternative representation for large dimensional data sets. Instead of using 2D or 3D representations, data is located on the surface of a sphere. Together with this representation, a hierarchical clustering algorithm is defined to analyse and extract the structure of the data. The algorithm builds a hierarchical structure (a dendrogram) in such a way that different cuts of the structure lead to different partitions of the surface of the sphere. This can be seen as a set of concentric spheres, each one being of different granularity. Also, to obtain an initial assignment of the data on the surface of the sphere, a method based on Sammon's mapping has been developed.Peer Reviewe

A definition for I-fuzzy partitions

In this paper, we define I-fuzzy partitions (or intuitionistic fuzzy partitions as called by Atanassov or interval-valued fuzzy partitions). As our ultimate goal is to compare the results of standard fuzzy clustering algorithms (e.g. fuzzy c-means), we define a method to construct them from a set of fuzzy clusters obtained from several executions of fuzzy c-means. From a practical point of view, the approach presented here tries to solve the difficulty of comparing the results of fuzzy clustering methods and, in particular, the difficulty of finding the global optimal. © 2010 Springer-Verlag.Partial support by the Spanish MEC (projects ARES – CONSOLIDER INGENIO 2010 CSD2007-00004 – and eAEGIS – TSI2007-65406-C03-02) is acknowledged.Peer Reviewe