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Bioinformatics and Biology Insights

Synopsis: An open access, peer reviewed electronic journal that covers computational biology, particularly computational methods used in the analysis and annotation of structures.


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About this journal

Aims and scope:

Bioinformatics and Biology Insights is an open access, peer-reviewed journal that considers articles on computational methods used in the analysis and annotation of structures, in addition to other areas of computational biology and the broader field of biology.

It both complements Libertas Academica’s subject-specific journals in the area and also seeks to place bioinformatics in the broader context of biology. The journal welcomes all submissions in the field of bioinformatics and also submissions dealing with the relationship between bioinformatics and the broader field of biology. Submissions of original research, reviews, tutorials, rapid communications, expert commentaries, letters, application notes, and point–counter-point articles are welcomed for peer review. No word limits are imposed, but authors are reminded that excessive word-counts may attract adverse comment by peer reviewers and discourage readers.

The submission of tutorial-type articles is encouraged, in which methods which have been developed in the recent past are reviewed in such a way as to make them readily comprehensible for Biologists. Papers discussing methodologies are discouraged unless they explicitly demonstrate that new biological insights have been gained or that earlier methods used to gain a new insight can be replaced.

Authors are encouraged to consider submitting their manuscripts to Evolutionary Bioinformatics and Cancer Informatics, if they consider that their manuscript is exclusively or specifically relevant to those journals’ audiences.

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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 is indexed by the following services:

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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: 1177-9322


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GSMA: Gene Set Matrix Analysis, An Automated Method for Rapid Hypothesis Testing of Gene Expression Data

Authors: Chris Cheadle, Tonya Watkins, Jinshui Fan, Marc A. Williams, Steven Georas, John Hall, Antony Rosen and Kathleen C. Barnes
Publication Date: 15 Oct 2007
Bioinformatics and Biology Insights 2007:1 49-62

Chris Cheadle1, Tonya Watkins1, Jinshui Fan1, Marc A. Williams2, Steven Georas2, John Hall3, Antony Rosen3 and Kathleen C. Barnes1

1Genomics Core, Division of Allergy and Clinical Immunology, School of Medicine, Johns Hopkins University, 5200 Eastern Avenue, Baltimore, MD 21224. 2University of Rochester School of Medicine and Dentistry, Division of Pulmonary and Critical Care Medicine, Rochester, New York, U.S.A. 3Division of Rheumatology, School of Medicine, Johns Hopkins University, 5200 Eastern Avenue, Baltimore, MD 21224.

Abstract

Background: Microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. The assignment of functional information to these complex patterns remains a challenging task in effectively interpreting data and correlating results from across experiments, projects and laboratories. Methods which allow the rapid and robust evaluation of multiple functional hypotheses increase the power of individual researchers to data mine gene expression data more efficiently.

Results: We have developed (gene set matrix analysis) GSMA as a useful method for the rapid testing of group-wise up- or downregulation of gene expression simultaneously for multiple lists of genes (gene sets) against entire distributions of gene expression changes (datasets) for single or multiple experiments. The utility of GSMA lies in its flexibility to rapidly poll gene sets related by known biological function or as designated solely by the end-user against large numbers of datasets simultaneously.

Conclusions: GSMA provides a simple and straightforward method for hypothesis testing in which genes are tested by groups across multiple datasets for patterns of expression enrichment.



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