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January 2006


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Francesco Masulli <[log in to unmask]>
Reply To:
Classification, clustering, and phylogeny estimation
Tue, 10 Jan 2006 16:05:21 +0100
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IEEE Symposium on Computer-Based Medical Systems (IEEE CBMS 2006)
Marriott Salt Lake City  City Center, Salt Lake City, Utah, USA,
June 22-23, 2006.

Special Track

Computational Proteomics: Management and Analysis of Proteomics Data

Special Track Website: 

Proteomics is a fastly developing area of biochemical investigation. The basic 
aim of proteomic analysis is the identification of specific protein patterns 
from cells, tissues and biological fluids related to physiological or 
pathological conditions. It provides a different view as compared to gene 
expression profiling, which does not evaluate post-transcriptional, 
post-translational modifications as well as protein compartimentalization and 
half-life changes (for instance ubiquitination and proteasome-driven 
degradation). All these characteristics make the protein profile much more 
complex but more informative compared to gene expression profiling.

Several approaches have been used to perform proteomic analysis; among them, 
technologies based on Mass Spectrometry (MS) have revolutionized proteomics 
and are heavily used to make high-throughput measurements for identifying 
macromolecules in a specific compound. Recently, Liottas group at the 
National Institutes of Health, USA, using SELDI-TOF mass spectrometry, has 
identified in the serum of patients with ovarian cancer a cluster pattern 
that completely segregated cancer from non-cancer. These results, 
characterized by a high degree of sensitivity and specificity, represent an 
extraordinary step forward in the early detection and diagnosis of ovarian 
cancer and justify a prospective population-based assessment of proteomic 
pattern technology as a screening tool for all stages of ovarian cancer in 
high-risk and general populations. Similar studies performed on different 
types of neoplastic diseases have confirmed the importance of identification 
of molecular profiles or signatures (either at RNA or protein level) as a 
powerful tool for innovative diagnostic and therapeutic approaches. 

This Special Track is designed to bring together computer scientists, 
biologists and clinicians for exploring the current state-of-the-art research 
taking place in all aspects of computational proteomics, from basic science 
to clinical practice. The track intends to provide a forum for the 
presentation of original research, valuable software tools (basic algorithms, 
modelling, analysis, and visualization tools, databases), and clinical 
fallouts, on topics of importance to computational proteomics, with special 
emphasis on MS-based approaches.

Matrix-Assisted Laser Desorption/Ionisation Time Of Flight (MALDI-TOF), 
Surface-Enhanced Laser Desorption/Ionization Time Of Flight (SELDI-TOF), and 
Liquid Chromatography (LC-MS/MS) are the main techniques. They separate gas 
phase ions according to their m/Z (mass to charge ratio) values producing 
huge volumes of data. MS output is represented, as a (large) sequence of 
value pairs, containing a measured intensity, which depends on the quantity 
of the detected biomolecules and a mass to charge ratio m/Z, which depends on 
the molecular mass of detected biomolecules. A MS experiment usually 
generates one or more datasets, said spectra, that contains a huge quantity 
of measurements (m/Z, intensity) said peaks. 

MS-based proteomics requires bioinformatics methods that can make biological 
inferences from both the raw experimental data sources (e.g., peptide 
identifications and mass measurements) and the resulting identifications. 
Prior to be analysed, spectra need to be cleaned, pre-processed, organized, 
and stored in an efficient way. Moreover, standard formats enabling 
laboratory interoperation and experiment replication are needed. The Human 
Proteome Organization-Proteomic Standard Initiative (HUPO-PSI) is defining 
novel standards for proteomics data and experiments such as mzData to store 
spectra data as well as metadata information about proteomics experiments, 
and mzIdent capturing parameters and results of search engines such as Mascot 
and Sequest.
On the other hand, alone or in combination with MS, fluorescence-based 
techniques are also used for proteomics analysis. Differential display 
proteomics increases the information content and throughput of proteomics 
studies through multiplexed analysis. Main approaches to differential display 
proteomics such as Difference Gel Electrophoresis (DIGE), Multiplexed 
Proteomics (MP), and Isotope-Coded Affinity Tagging (ICAT), greatly enhance 
the applicability of the Two-Dimensional Gel Electrophoresis technique 
Representation, organization and analysis of fluorescence-based proteomics 
data, such as 2D-Gel data, are also important topics.

Genomic and Proteomic data need to be analyzed in combination. The full 
realization of the concept of genomic medicine, where genomics and proteomics 
are used to improve healthcare, requires the integration of knowledge from 
biology and medicine. To exploit the data available in research centres and 
care facilities new frameworks integrating data, computational methods and 
tools have to be provided, that bridge bioinformatics, biology and medicine. 
The semantic integration and further analysis of genomic, proteomics and 
clinical data is a first step toward the deployment of novel biomedical 
applications involving research-oriented competence centres, specialized 
proteomics facilities, and
health centres where clinical guidelines are applied.

Key issues to be faced when considering integrated management and analysis of 
MS proteomics data are:
* spectra data models and databases;
* spectra pre-processing;
* spectra analysis (e.g. peptide identification, protein identification, 
* spectra visualization;
* integration and interaction with biological data banks (e.g. sequence, 
structure, and peptide data banks);
* data models and databases for integrated genomics and proteomics data;
* integrated analysis of genomic and proteomic data with clinical/instrumental 
* identification of new biomarkers for early diagnosis and tailored 

Methods, algorithms and techniques for proteomics data organization, storage 
and analysis, as well as the use of proteomics methods and techniques in 
clinical practice are the key topics of this Special Track. Whatever 
technique being used, proteomics data are huge, involve heterogeneous 
platforms and require high performance computing, so presentation of high 
performance and Grid-based computational methods, as well as semantic-rich 
methods for in silico workflow composition are welcomed. Examples of areas of 
interest include, but are not restricted to:

* Spectra data models and databases
* Spectra-related data banks (e.g. Mascot, Sequest) 
* Spectra pre-processing algorithms
* Peptide/protein identification
* Protein-protein interactions
* Data Mining techniques for proteomics data
* Statistical analysis of proteomics data
* Image analysis and visualization of proteomics data
* 2-D Gel proteomics data and analysis
* Virtual Proteomics Laboratory
* Data integration and proteomics 
* Technologies and models to store phenotype, genotype and proteotype data;
* Integration of proteomics and clinical data for diagnosis and treatment
* Application of proteomics methods in clinical practice
* Workflow of proteomics experiments
* Knowledge Management and ontologies in proteomics
* Parallel and Grid-based computational proteomics
* Standards in proteomics

January 31, 2006 Deadline for paper submissions
March 1, 2006  Notification of acceptance
April 5, 2006  Final camera-ready paper (6 pages, maximum) due
April 8, 2006  Pre-registration deadline 
May 22, 2006  Hotel room reservations due
June 22-23, 2006 Conference

Unlike workshops, where position papers and reports on initial and intended 
work are appropriate, papers selected for a special track should report on 
significant unpublished work suitable for publication as a conference paper.
Interested authors should submit a 3-6 pages manuscript (or approximately 
1500-3000 words) clearly describing background, goals, methods, results and a 
listing of primary references. Presented material should include sufficient 
detail to enable the program committee in reviewing the article. 

The article should be composed on US Letter page format and submitted as a PDF 
or Microsoft Word document. The article review is double blind and as such 
personally identifying information is discouraged. Author name and address 
should be withheld from the article text for review purposes. Article text 
should be in font size of at least 10 point. Authors should indicate the 
Special Track title in their submissions. All submissions including special 
track papers will be done electronically via the CBMS web submission system, 
which will be open approximately one month before the deadline.
Please note that the format of IEEE CBMS 2006 proceedings will be the IEEE 
Computer Science Press 8.5x11-inch format, available at: 
For more details please see the website of IEEE CBMS 2006:

* Mario Cannataro (University Magna Gr�ia of Catanzaro, Italy)
* Giovanni Cuda  (University Magna Gr�ia of Catanzaro, Italy)
* Pierangelo Veltri (University Magna Gr�ia of Catanzaro, Italy)

* Alberto Apostolico  (Georgia Institute of Technology, USA, and University of 
Padova, Italy)
* Gerard Cagney  (Conway Institute, University College Dublin, Ireland)
* Tim Clark   (Harvard Medical School -  MassGeneral Institute for 
Neurodegenerative Disease, USA)
* David De Roure  (University of Southampton, UK)
* Giuseppe Di Fatta  (ICAR-CNR, Italy, and University of Konstanz, Germany)
* Marco Gaspari  (University Magna Gr�ia of Catanzaro, Italy)
* Carole Goble   (University of Manchester, UK)
* Concettina Guerra  (University of Padova, Italy)
* Des Higgins   (Conway Institute, University College Dublin, Ireland)
* Hasan Jamil   (Wayne State University, Michigan, USA)
* Maria Mirto  (University of Lecce, Italy)
* Giovanni Morrone  (University Magna Gr�ia of Catanzaro, Italy)
* Luigi Palopoli  (University of Calabria, Italy)
* Helen Parkinson  (European Bioinformatics Institute, UK)
* Stephen Pennington  (Conway Institute, University College Dublin, Ireland)
* Emmanuel F. Petricoin (National Cancer Institute, USA)
* Omer F. Rana   (Cardiff University, UK)
* Roberto Tagliaferri  (University of Salerno, Italy)
* Domenico Talia  (University of Calabria, Italy)
* Chris Taylor   (European Bioinformatics Institute, UK)
* Rosa Terracciano  (University Magna Gr�ia of Catanzaro, Italy)
* Gordon R. Whiteley  (National Cancer Institute, USA)