[Computational-biology] Second Issue of International Journal of Knowledge Discovery in Bioinformatics (IJKDB)

Li Xiaoli via comp-bio%40net.bio.net (by xlli from i2r.a-star.edu.sg)
Thu Aug 12 00:33:11 EST 2010

Dear Colleages, 

We are happy to announce that the Second Issue of International Journal
of Knowledge Discovery in Bioinformatics (IJKDB) has been published.
The table of content of the second issue is attached below.  

1. PAPER ONE : SPCCTDM, a Catalogue for Analysis of Therapeutic Drug
Monitoring Related Contents in the Drug Prescription Information


Sven Ulrich, Pharmaceutical Consultant, Germany
Pierre Baumann, University of Lausanne, Switzerland
Andreas Conca, Regional Hospital of Bolzano, Italy
Hans-Joachim Kuss, University of Munich, Germany
Viktoria Stieffenhofer, University of Mainz, Germany
Christoph Hiemke, University of Mainz, Germany


Therapeutic drug monitoring (TDM) has consistently been shown to be
useful for optimization of drug therapy. For the first time, a method
has been developed for the text analysis of TDM in SPCs in that a
catalogue SPC-ContentTDM (SPCCTDM) provides a codification of the
content of TDM in SPCs. It consists of six structure-related items
(dose, adverse drug reactions, drug interactions, overdose,
pregnancy/breast feeding, and pharmacokinetics) according to implicit or
explicit references to TDM in paragraphs of the SPC, and four
theory-guided items according to the information about ranges of plasma
concentrations and a recommendation of TDM in the SPC. The catalogue is
regarded as valid for the text analysis of SPCs with respect to TDM. It
can be used in the comparison of SPCs, in the comparison with
medico-scientific evidence and for the estimation of the perception of
TDM in SPCs by the reader. Regarding the approach as a model of text
mining, it may be extended for evaluation of other aspects reported in


To obtain a copy of the entire article, click on the link below.



2. PAPER TWO : Clustering Genes Using Heterogeneous Data Sources


Erliang Zeng, University of Notre Dame, USA
Chengyong Yang (Life Technologies Inc., USA
Tao Li (Florida International University, USA
Giri Narasimhan (Florida International University, USA


Clustering of gene expression data is a standard exploratory technique
used to identify closely related genes. Many other sources of data are
also likely to be of great assistance in the analysis of gene expression
data. This data provides a mean to begin elucidating the large-scale
modular organization of the cell. The authors consider the challenging
task of developing exploratory analytical techniques to deal with
multiple complete and incomplete information sources. The Multi-Source
Clustering (MSC) algorithm developed performs clustering with multiple,
but complete, sources of data. To deal with incomplete data sources, the
authors adopted the MPCK-means clustering algorithms to perform
exploratory analysis on one complete source and other potentially
incomplete sources provided in the form of constraints. This paper
presents a new clustering algorithm MSC to perform exploratory analysis
using two or more diverse but complete data sources, studies the
effectiveness of constraints sets and robustness of the constrained
clustering algorithm using multiple sources of incomplete biological
data, and incorporates such incomplete data into constrained clustering
algorithm in form of constraints sets.


To obtain a copy of the entire article, click on the link below.



3. PAPER THREE :Infer Species Phylogenies Using Self-Organizing Maps


Xiaoxu Han, Eastern Michigan University, USA


With rapid advances in genomics, phylogenetics has turned to
phylogenomics due to the availability of large amounts of sequence and
genome data. However, incongruence between species trees and gene trees
remains a challenge in molecular phylogenetics for its biological and
algorithmic complexities. A state-of-the-art gene concatenation approach
was proposed to resolve this problem by inferring the species phylogeny
using a random combination of widely distributed orthologous genes
screened from genomes. However, such an approach may not be a robust
solution to this problem because it ignores the fact that some genes are
more informative than others in species inference. This paper presents a
self-organizing map (SOM) based phylogeny inference method to overcome
its weakness. The author's proposed algorithm not only demonstrates its
superiority to the original gene concatenation method by using same
datasets, but also shows its advantages in generalization. This paper
illustrates that data missing may not play a negative role in phylogeny
inferring. This study presents a method to cluster multispecies genes,
estimate multispecies gene entropy and visualize the species patterns
through the self-organizing map mining.


To obtain a copy of the entire article, click on the link below.



4. PAPER FOUR : Wave-SOM: A Novel Wavelet-Based Clustering Algorithm for
Analysis of Gene Expression Patterns


Andrew Blanchard, University of Arkansas, USA
Christopher Wolter, University of Arkansas & University of Minnesota,
David McNabb, University of Arkansas, USA
Eitan Gross, University of Arkansas, USA


In this paper, the authors present a wavelet-based algorithm (Wave-SOM)
to help visualize and cluster oscillatory time-series data in
two-dimensional gene expression micro-arrays. Using various wavelet
transformations, raw data are first de-noised by decomposing the
time-series into low and high frequency wavelet coefficients. Following
thresholding, the coefficients are fed as an input vector into a
two-dimensional Self-Organizing-Map clustering algorithm. Transformed
data are then clustered by minimizing the Euclidean (L2) distance
between their corresponding fluctuation patterns. A multi-resolution
analysis by Wave-SOM of expression data from the yeast Saccharomyces
cerevisiae, exposed to oxidative stress and glucose-limited growth,
identified 29 genes with correlated expression patterns that were mapped
into 5 different nodes. The ordered clustering of yeast genes by
Wave-SOM illustrates that the same set of genes (encoding ribosomal
proteins) can be regulated by two different environmental stresses,
oxidative stress and starvation. The algorithm provides heuristic
information regarding the similarity of different genes. Using
previously studied expression patterns of yeast cell-cycle and
functional genes as test data sets, the authors' algorithm outperformed
five other competing programs.


To obtain a copy of the entire article, click on the link below.




For full copies of the above articles, check for this issue of the
International Journal of Knowledge Discovery in Bioinformatics (IJKDB)
in your institution's library. This journal is also included in the IGI
Global aggregated "InfoSci-Journals" database:
<http://www.igi-global.com/EResources/InfoSciJournals.aspx> . 









1. Mission of IJKDB:

The mission of the International Journal of Knowledge Discovery in
Bioinformatics (IJKDB) is to increase awareness of interesting and
challenging biomedical problems and to inspire new knowledge discovery
solutions, which can be translated into further biological and clinical
studies. IJKDB is aimed at researchers in the areas of bioinformatics,
knowledge discovery, machine learning, and data structure, as well as
practitioners in the life sciences industry. In addition to original
research papers in bioinformatics, this journal emphasizes software and
tools that exploit the knowledge discovery techniques to address
biological problems and databases that contain useful biomedical data
generated in wet and dry labs. IJKDB encompasses discovery notes that
report newly found biological discoveries using computational techniques
and includes reviews and tutorials on relevant computational and
experimental techniques for translational research and knowledge
discovery in life sciences.


2. Coverage of IJKDB:


Topics to be discussed in this journal include (but are not limited to)
the following:   

       *         Bioimage analysis 

*         Bioinformatics databases 

*         Biological data and text mining algorithms 

*         Biological data collection and cleansing 

*         Biological data integration 

*         Biological data management 

*         Biological knowledge discovery 

*         Biological knowledge visualization 

*         Biological networks (protein interaction, metabolic,
transcription factor, signaling, etc.) 

*         Biological tools/applications 

*         Biostatistics 

*         Clinical research informatics 

*         Computational evolutionary biology 

*         Data mining and its applications in bioinformatics 

*         Disease bioinformatics 

*         Drug discovery 

*         Gene expression analysis 

*         Gene regulation 

*         Genome annotation 

*         Integration of biological and clinical data 

*         Molecular evolution and phylogeny 

*         Ontology 

*         Probability theory 

*         Protein/RNA structure prediction 

*         Sequence analysis 

*         Statistics and its applications 

*         Structural proteomics 

*         Systems biology 

*         Translational bioinformatics 

3. Review Board  

International Advisory Board 
Philip E. Bourne, University of California San Diego, USA
Satoru Miyano, University of Tokyo, Japan
George Perry, University of Texas at San Antonio, USA
Anna Tramontano, Sapienza University, Italy
Philip S. Yu, University of Illinois at Chicago, USA  

Associate Editors 
Zhang Aidong, State University of New York at Buffalo (UB), USA
Tatsuya Akutsu, Bioinformatics Center - Institute for Chemical Research
at Kyoto University, Japan
William CS Cho, Queen Elizabeth Hospital, Hong Kong 
Peter Clote, Boston College, USA
Eytan Domany, Weizmann Institute of Science, Israel
Wen-Lian Hsu, Academia Sinica, Taiwan
Igor Jurisica, University of Toronto - Ontario Cancer Institute, Canada
Samuel Kaski, Helsinki University of Technology, Finland
Daisuke Kihara, Purdue University, USA
Hiroshi Mamitsuka, Bioinformatics Center - Institute for Chemical
Research at Kyoto University, Japan
George Perry, University of Texas at San Antonio, USA
Narayanaswamy Srinivasan, Molecular Biophysics Unit - Indian Institute
of Science, India
Alfonso Valencia, National Cancer Research Center, Spain
Jason T.L. Wang, New Jersey Institute of Technology, USA
Lusheng Wang, City University of Hong Kong, China
Wei Wang, University of North Carolina at Chapel Hill, USA
Mohammed J. Zaki, Rensselaer Polytechnic Institute, USA 


Interested authors should consult the journal's manuscript submission
guidelines at www.igi-global.com/ijkdb <http://www.igi-global.com/ijkdb>


International Journal of Knowledge Discovery in Bioinformatics (IJKDB)

Official Publication of the Information Resources Management Association

Volume 1, Issue 2, April-June 2010

Published: Quarterly in Print and Electronically

ISSN: 1947-9115 EISSN: 1947-9123

Published by IGI Publishing, Hershey-New York, USA

www.igi-global.com/ijkdb <http://www.igi-global.com/ijkdb> 



4. Journal submission website: 


All inquiries should be sent to:

Editor-in-Chief: Xiao-Li Li at xlli from i2r.a-star.edu.sg
<mailto:xlli from i2r.a-star.edu.sg>  and See-Kiong Ng at
skng from i2r.a-star.edu.sg <mailto:skng from i2r.a-star.edu.sg> 





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