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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.  

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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: 1177-9322


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Association Between a Prognostic Gene Signature and Functional Gene Sets

Authors: Manuela Hummel, Klaus H. Metzeler, Christian Buske, Stefan K. Bohlander and Ulrich Mansmann
Publication Date: 22 Sep 2008
Bioinformatics and Biology Insights 2008:2 335-347

Manuela Hummel1,2, Klaus H. Metzeler3, Christian Buske3, Stefan K. Bohlander3 and Ulrich Mansmann1,4

1Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Germany. 2Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), Barcelona, Spain. 3Laboratory of Leukemia Diagnostics, Department of Internal Medicine III, University Hospital Großhadern, University of Munich, Germany. 4Department of Statistics, University of Munich, Germany.

Abstract

Background:  The development of expression-based gene signatures for predicting prognosis or class membership is a popular and challenging task. Besides their stringent validation, signatures need a functional interpretation and must be placed in a biological context. Popular tools such as Gene Set Enrichment have drawbacks because they are restricted to annotated genes and are unable to capture the information hidden in the signature’s non-annotated genes.

Methodology:  We propose concepts to relate a signature with functional gene sets like pathways or Gene Ontology categories. The connection between single signature genes and a specific pathway is explored by hierarchical variable selection and gene association networks. The risk score derived from an individual patient’s signature is related to expression patterns of pathways and Gene Ontology categories. Global tests are useful for these tasks, and they adjust for other factors. GlobalAncova is used to explore the effect on gene expression in specific functional groups from the interaction of the score and selected mutations in the patient’s genome.

Results:  We apply the proposed methods to an expression data set and a corresponding gene signature for predicting survival in Acute Myeloid Leukemia (AML). The example demonstrates strong relations between the signature and cancer-related pathways. The signature-based risk score was found to be associated with development-related biological processes.

Conclusions:  Many authors interpret the functional aspects of a gene signature by linking signature genes to pathways or relevant functional gene groups. The method of gene set enrichment is preferred to annotating signature genes to specific Gene Ontology categories. The strategies proposed in this paper go beyond the restriction of annotation and deepen the insights into the biological mechanisms reflected in the information given by a signature.



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