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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"> <HTML xmlns:o = "urn:schemas-microsoft-com:office:office" xmlns:st1 = "urn:schemas-microsoft-com:office:smarttags"><HEAD> <META http-equiv=Content-Type content="text/html; charset=us-ascii"> <META content="MSHTML 6.00.6000.16809" name=GENERATOR></HEAD> <BODY> <DIV dir=ltr align=left>Dear Colleages, </DIV> <DIV dir=ltr align=left> <o:p></o:p></DIV> <DIV dir=ltr align=left> <o:p></o:p> 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. <o:p> </o:p> 1. PAPER ONE : SPCCTDM, a Catalogue for Analysis of Therapeutic Drug Monitoring Related Contents in the Drug Prescription Information<o:p></o:p> <o:p> </o:p> 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<o:p></o:p> <o:p> </o:p> 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.<o:p></o:p> <o:p> </o:p> To obtain a copy of the entire article, click on the link below.<o:p></o:p> <A href="http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45162">http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45162</A><o:p></o:p> <o:p> </o:p> 2. PAPER TWO : Clustering Genes Using Heterogeneous Data Sources<o:p></o:p> <o:p> </o:p> 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<o:p></o:p> <o:p> </o:p> 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.<o:p></o:p> <o:p> </o:p> To obtain a copy of the entire article, click on the link below.<o:p></o:p> <A href="http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45163">http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45163</A><o:p></o:p> <o:p> </o:p> 3. PAPER THREE :Infer Species Phylogenies Using Self-Organizing Maps<o:p></o:p> <o:p> </o:p> <st1:City w:st="on">Xiaoxu Han</st1:City>, <st1:State w:st="on">Eastern</st1:State> <st1:State w:st="on">Michigan</st1:State> University, <st1:country-region w:st="on"><st1:place w:st="on">USA</st1:place></st1:country-region><o:p></o:p> <o:p> </o:p> 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.<o:p></o:p> <o:p> </o:p> To obtain a copy of the entire article, click on the link below.<o:p></o:p> <A href="http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45164">http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45164</A><o:p></o:p> <o:p> </o:p> 4. PAPER FOUR : Wave-SOM: A Novel Wavelet-Based Clustering Algorithm for Analysis of Gene Expression Patterns<o:p></o:p> <o:p> </o:p> 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<o:p></o:p> <o:p> </o:p> 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.<o:p></o:p> <o:p> </o:p> To obtain a copy of the entire article, click on the link below.<o:p></o:p> <A href="http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45165">http://www.igi-global.com/Bookstore/Article.aspx?TitleId=45165</A><o:p></o:p> <o:p> </o:p> *****************************************************<o:p></o:p> 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: <A href="http://www.igi-global.com/EResources/InfoSciJournals.aspx">http://www.igi-global.com/EResources/InfoSciJournals.aspx</A>. <o:p></o:p> *****************************************************<o:p></o:p> <o:p> </o:p>  <o:p></o:p>  <o:p></o:p> <o:p> </o:p> =======================================================================================<o:p></o:p> CALL FOR PAPERS<o:p></o:p> <o:p> </o:p> 1. <st1:City w:st="on"><st1:place w:st="on">Mission</st1:place></st1:City> of IJKDB:<o:p></o:p> 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.<o:p></o:p> <o:p> </o:p> 2. Coverage of IJKDB:<o:p></o:p> <o:p> </o:p> Topics to be discussed in this journal include (but are not limited to) the following:   <o:p></o:p>        ·         Bioimage analysis <o:p></o:p> ·         Bioinformatics databases <o:p></o:p> ·         Biological data and text mining algorithms <o:p></o:p> ·         Biological data collection and cleansing <o:p></o:p> ·         Biological data integration <o:p></o:p> ·         Biological data management <o:p></o:p> ·         Biological knowledge discovery <o:p></o:p> ·         Biological knowledge visualization <o:p></o:p> ·         Biological networks (protein interaction, metabolic, transcription factor, signaling, etc.) <o:p></o:p> ·         Biological tools/applications <o:p></o:p> ·         Biostatistics <o:p></o:p> ·         Clinical research informatics <o:p></o:p> ·         Computational evolutionary biology <o:p></o:p> ·         Data mining and its applications in bioinformatics <o:p></o:p> ·         Disease bioinformatics <o:p></o:p> ·         Drug discovery <o:p></o:p> ·         Gene expression analysis <o:p></o:p> ·         Gene regulation <o:p></o:p> ·         Genome annotation <o:p></o:p> ·         Integration of biological and clinical data <o:p></o:p> ·         Molecular evolution and phylogeny <o:p></o:p> ·         Ontology <o:p></o:p> ·         Probability theory <o:p></o:p> ·         Protein/RNA structure prediction <o:p></o:p> ·         Sequence analysis <o:p></o:p> ·         Statistics and its applications <o:p></o:p> ·         Structural proteomics <o:p></o:p> ·         Systems biology <o:p></o:p> ·         Translational bioinformatics <o:p></o:p> <DIV style="BORDER-RIGHT: medium none; PADDING-RIGHT: 0pt; BORDER-TOP: medium none; PADDING-LEFT: 0pt; BACKGROUND: white; PADDING-BOTTOM: 0pt; BORDER-LEFT: medium none; PADDING-TOP: 0pt; BORDER-BOTTOM: #eeeeee 1pt solid; mso-border-bottom-alt: solid #EEEEEE .75pt; mso-element: para-border-div"> <H4 style="BACKGROUND: white; MARGIN: 15pt 0pt 3.75pt">3. Review Board  <o:p></o:p></H4> <H4 style="MARGIN: 15pt 0pt 3.75pt">International Advisory Board Philip E. Bourne, <st1:PlaceType w:st="on">University</st1:PlaceType> of <st1:PlaceName w:st="on">California</st1:PlaceName> <st1:City w:st="on">San Diego</st1:City>, <st1:country-region w:st="on">USA</st1:country-region> Satoru Miyano, <st1:City w:st="on">University of Tokyo</st1:City>, <st1:country-region w:st="on">Japan</st1:country-region> George Perry, <st1:PlaceType w:st="on">University</st1:PlaceType> of <st1:PlaceName w:st="on">Texas</st1:PlaceName> at <st1:City w:st="on">San Antonio</st1:City>, <st1:country-region w:st="on">USA</st1:country-region> Anna Tramontano, <st1:City w:st="on">Sapienza University</st1:City>, <st1:country-region w:st="on">Italy</st1:country-region> Philip S. Yu, <st1:PlaceType w:st="on">University</st1:PlaceType> of <st1:PlaceName w:st="on">Illinois</st1:PlaceName> at <st1:place w:st="on"><st1:City w:st="on">Chicago</st1:City>, <st1:country-region w:st="on">USA</st1:country-region></st1:place>  <o:p></o:p></H4> <H4 style="MARGIN: 15pt 0pt 3.75pt">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 <o:p></o:p></H4></DIV> <o:p> </o:p> Interested authors should consult the journal's manuscript submission guidelines at <A href="http://www.igi-global.com/ijkdb">www.igi-global.com/ijkdb</A>. <o:p></o:p> <o:p> </o:p> International Journal of Knowledge Discovery in Bioinformatics (IJKDB)<o:p></o:p> Official Publication of the Information Resources Management Association<o:p></o:p> Volume 1, Issue 2, April-June 2010<o:p></o:p> Published: Quarterly in Print and Electronically<o:p></o:p> ISSN: 1947-9115 EISSN: 1947-9123<o:p></o:p> Published by IGI Publishing, Hershey-<st1:place w:st="on"><st1:City w:st="on">New York</st1:City>, <st1:country-region w:st="on">USA</st1:country-region></st1:place><o:p></o:p> <A href="http://www.igi-global.com/ijkdb">www.igi-global.com/ijkdb</A><o:p></o:p>  <o:p></o:p>  <o:p></o:p> 4. Journal submission website: <o:p></o:p> <A href="http://datam.i2r.a-star.edu.sg/ijkdb/">http://datam.i2r.a-star.edu.sg/ijkdb/</A> <o:p></o:p> All inquiries should be sent to:<o:p></o:p> Editor-in-Chief: Xiao-Li Li at <A href="mailto:xlli@i2r.a-star.edu.sg">xlli@i2r.a-star.edu.sg</A> and See-Kiong Ng at <A href="mailto:skng@i2r.a-star.edu.sg">skng@i2r.a-star.edu.sg</A><o:p></o:p> <o:p> </o:p></DIV> <DIV dir=ltr align=left> <o:p> </o:p></DIV> <o:p> </o:p> <DIV> </DIV> <DIV> Institute for Infocomm Research disclaimer: "This email is confidential and may be privileged. If you are not the intended recipient, please delete it and notify us immediately. Please do not copy or use it for any purpose, or disclose its contents to any other person. Thank you." </DIV></BODY></HTML>

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