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 SPCs.

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

http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45162

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.

http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45163

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.

http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45164

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, USA
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.

http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45165

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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.

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CALL FOR PAPERS

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 

Interested authors should consult the journal's manuscript submission guidelines at 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

4. Journal submission website:

http://datam.i2r.a-star.edu.sg/ijkdb/

All inquiries should be sent to:

Editor-in-Chief: Xiao-Li Li at [log in to unmask] and See-Kiong Ng at [log in to unmask]