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Cancer Informatics

Synopsis: An open access, peer reviewed electronic journal that covers the role of computational biology and bioinformatics in cancer treatment.


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About this journal

Aims and scope:

We live in a time when it is widely recognized that scientific collaborations across traditional disciplines can yield exponential gains through synergy. Cancer research is currently benefiting from advances in many fields, including biology (genomics and proteomics), physical chemistry (mass spectrometry and radio imaging), computer science and biostatistics (machine learning, artificial intelligence), and many others.

There exists a bewildering diversity of scientific journals in which new applications of these advances toward discovery in cancer research is reported. Leveraging these advances into new medicines and medical practices for the early detection, prevention and treatment of cancer is made difficult by the broad diffusion this literature. Bioinformatics and computational biology appear to play a central role at each nexus, in part because novel technologies lead to immense leaps in the amount and granularity of data from patients and patient samples.

A number of important journals exist that focus on a wide breadth of foci within bioinformatics. Medical informatics is a field nearly as wide that includes patient information systems and related critical components of information management in the information age.

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Submissions, excluding editorials, letters to the editor and dedications, will be peer reviewed by two reviewers.  Reviewers are required to provide fair, balanced and constructive reports.  

Under our Fairness in Peer Review Policy authors may appeal against reviewers' recommendations which are ill-founded, unobjective or unfair.  Appeals are considered by the Editor in Chief or Associate Editor.

Papers are not sent to peer reviewers following submission of a revised manuscript. Editorial decisions on re-submitted papers are based on the author's response to the initial peer review report.

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This journal has been awarded a SPARC Europe Seal. The Seal is an initiative of SPARC Europe (Scholarly Publishing and Academic Resources Coalition) and the Directory of Open Access Journals (DOAJ) which is awarded to journals applying a Creative Commons CC-BY copyright license and that make journal metadata accessible to DOAJ.  

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National Institutes of Health Public Access Policy compliant:

As of April 7 2008, the US NIH Public Access Policy requires that all peer reviewed articles resulting from research carried out with NIH funding be deposited in the Pubmed Central archive.

If you are an NIH employee or grantee Libertas Academica will ensure that you comply with the policy by depositing your paper at Pubmed Central on your behalf. 

ISSN: 1176-9351


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Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

Authors: Nadège Dossat, Alain Mangé, Jérôme Solassol, William Jacot, Ludovic Lhermitte, Thierry Maudelonde, Jean-Pierre Daurès and Nicolas Molinari
Publication Date: 19 Jul 2007
Cancer Informatics 2007:3 295-305

Nadège Dossat1,2, Alain Mangé2,3,5, Jérôme Solassol2,3,5, William Jacot2,4, Ludovic Lhermitte2,3,5, Thierry Maudelonde2,3,5, Jean-Pierre Daurès1,2,6 and Nicolas Molinari1,2,6

1IURC, Department of Biostatistic, Epidemiology and Clinical Research, Montpellier, France. 2University of Montpellier I, Montpellier, France. 3CHU Montpellier, Hôpital Arnaud de Villeneuve, Department of Cellular Biology, Montpellier, France. 4CHU Montpellier, Hôpital Arnaud de Villeneuve, Department of Thoracic Oncology, Montpellier, France. 5INSERM, U540, Montpellier, France 6Chu Nîmes, Hôspital Caremeau, Department of Medical Information, Nîmes, France

Summary: A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The fi rst two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classifi cation Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.



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