14/07/2011Genome structure analysis of molluscs by the members is now on Gene
Genome structure analysis of molluscs by Yoshida, Sese, Ogura et al. is published on Gene.
http://www.sciencedirect.com/science/article/pii/S0378111911002526
16/03/2011Further reduction of the computation power
We further curtailed the computation power of the center to less than 20 percent. This will significantly affect the web-server. Sorry for the inconvenience.
http://www.nytimes.com/2011/03/16/world/asia/16japan.html?hp
13/03/2011Reduce computation power of the center to counteract electricity supply reduction
We decide to reduce the computation power of the center to avoid a brown out in Tokyo area caused by electricity supply shortage due to Tohoku-Kanto Earthquake. This may affect the web-server. Sorry for the inconvenience.
http://www.japantimes.co.jp/news/quake.html
10/02/2011A new method to calculated gap penalty in amino acid sequence alignment by Prof. Kei Yura et al. is published in Proteins: Structure, Function, and Bioinformatics
Amino acid sequence alignment is a fundamental technology to convert genome information to protein structure and function information. An accurate alignment is a basis for comparative modelling. This paper proposes a new method to improve alignment by adjusting the location of gaps and shows the effect of the improvement in homology modelling.
http://onlinelibrary.wiley.com/doi/10.1002/prot.23011/abstract
07/01/2010Semi-supervised local Fisher discriminant analysis for dimensionality reduction by Prof. Sese is published in Machine Learning
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly because of overfitting. We propose a semi-supervised dimensionality reduction method which preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The proposed method has an analytic form of the globally optimal solution and it can be computed based on eigen-decomposition. We show the usefulness of the new method with real-world document classification datasets.
http://www.springerlink.com/content/80r2m583ph703632/
14/11/2009Web server shutdown for system maintenance
The web server will be down from 21:00, 22nd, Nov. to 21:00, 24th, Nov. for the system maintenance.
http://cib.cf.ocha.ac.jp/index-e.html
27/10/2009Workshop: Bioinformatics beyond Omics Data Analyses
The center will have an international workshop from 3rd to 4th of Dec, 2009. The workshop welcomes poster presentation by students and PostDocs.
http://cib.cf.ocha.ac.jp/symposium-e.html
25/9/2009Journal Paper by Kei Yura and Steven Hayward is accepted by
Bioinformatics
The manuscript "The interwinding nature of protein-protein interfaces and its implication for protein complex formation " by Kei Yura and Steven Hayward is accepted by Bioinformatics.
http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btp563?ijkey=voOBQz3LSrhqdM2&keytype=ref
25/9/2009Journal Paper by Kei Yura, Sintawee Sulaiman et al. is accepted by
Plant and Cell Physiology
The manuscript "RESOPS: A Database for Analyzing the Correspondence of RNA Editing Sites to Protein Three-Dimensional Structures" by Kei Yura, Sintawee Sulaiman et al. is accepted by Plant and Cell Physiology.
http://pcp.oxfordjournals.org/cgi/content/abstract/50/11/1865
1/8/2009Journal Paper by Prof Kato is accepted by
Bioinformatics
The existing supervised methods for biological network inference
are based only on intra-species information such as gene expression
data. The proposed method exploits cross-species information
in a theoretically well-appointed framework.
http://www.net-machine.net/~katolab/en/misc.html
14/7/2009Journal Paper by Prof Kato is accepted by IJKDB
Inferring the relationship among proteins is a central issue of
computational biology and a diversity of biological assays are utilized
to predict the relationship. However, as experiments are usually
expensive to perform, automatic data selection is employed to reduce the
data collection cost. Although data useful for link prediction are
different in each local sub-network, existing methods cannot select
different data for different processes. Prof Kato et al. have devised a
new algorithm for inferring biological networks from multiple types of
assays. The proposed algorithm is based on transfer learning and can
exploit local information effectively. Each assay is automatically
weighted through learning and the weights can be adaptively different in
each local part.
http://www.net-machine.net/~katolab/en/netinf3.html