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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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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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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.

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ISSN: 1176-9351


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Bias, Randomization, and Ovarian Proteomic Data: A Reply to "Producers and Consumers"

Authors: Keith A. Baggerly, Kevin R. Coombes, and Jeffrey S. Morris
Publication Date: 26 Feb 2007
Cancer Informatics 2005:1 9-14

Keith A. Baggerly, Kevin R. Coombes, and Jeffrey S. Morris

Department of Biostatistics, U.T. M.D. Anderson Cancer Center, Houston, Texas, USA

Abstract: Proteomic patterns derived from mass spectrometry have recently been put forth as potential biomarkers for the early diagnosis of cancer. This approach has generated much excitement, particularly as initial results reported on SELDI profiling of serum suggested that near perfect sensitivity and specificity could be achieved in diagnosing ovarian cancer. However, more recent reports have suggested that much of the observed structure could be due to the presence of experimental bias. A rebuttal to the findings of bias, subtitled “Producers and Consumers”, lists several objections. In this paper, we attempt to address these objections. While we continue to find evidence of experimental bias, we emphasize that the problems found are associated with experimental design and processing, and can be avoided in future studies.



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