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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.


Indexing: 6 major databases. Pubmed indexing for NIH-funded research.

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About this journal

ISSN: 1177-9322


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.

Editorial standards and procedures:

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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SPARC Europe Seal award winner:

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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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. 



 
 
 


Topological Properties of Co-Occurrence Networks in Published Gene Expression Signatures

Authors: Heiko Muller and Francesco Acquati
Publication Date: 17 Apr 2008
Bioinformatics and Biology Insights 2008:2 203-213

Heiko Muller1,2 and Francesco Acquati3

1The FIRC Institute of Molecular Oncology Foundation, Via Adamello 16, 20139 Milan, Italy, 2Department of Experimental Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milan, Italy, 3Department of Biotechnology and Molecular Sciences, University of Varese, Via Dunant 3, 21100 Varese, Italy

Abstract

Meta-analysis of high-throughput gene expression data is often used for the interpretation of proprietary gene expression data sets. We have recently shown that co-occurrence patterns of gene expression in published cancer-related gene expression signatures are reminiscent of several cancer signaling pathways. Indeed, significant co-occurrence of up to ten genes in published gene expression signatures can be exploited to build a co-occurrence network from the sets of co-occurring genes (“co-occurrence modules”). Such co-occurrence network is represented by an undirected graph, where single genes are assigned to vertices and edges indicate that two genes are significantly co-occurring. Thus, graph-cut methods can be used to identify groups of highly interconnected vertices (“network communities”) that correspond to sets of genes that are significantly co-regulated in human cancer. Here, we investigate the topological properties of co-occurrence networks derived from published gene expression signatures and show that co-occurrence networks are characterized by scale-free topology and hierarchical modularity. Furthermore, we report that genes with a “promiscuous” or a “faithful” co-occurrence pattern can be distinguished. This behavior is reminiscent of date and party hubs that have been identified in protein-protein interaction networks.

 



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