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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

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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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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Gene Copy Number Analysis for Family Data Using Semiparametric Copula Model

Authors: Ao Yuan, Guanjie Chen, Zhong-Cheng Zhou, George Bonney and Charles Rotimi
Publication Date: 26 Sep 2008
Bioinformatics and Biology Insights 2008:2 349-361

Ao Yuan1, Guanjie Chen2, Zhong-Cheng Zhou3, George Bonney1 and Charles Rotimi2

1National Human Genome Center, Howard University, U.S.A. 2Center for Research on Genomics Global Health, NHGRI, NIH, U.S.A. 3SuiZhou Central Hospital, SuiZhou, HuBei, 441300 P.R. China.

Abstract

Gene copy number changes are common characteristics of many genetic disorders. A new technology, array comparative genomic hybridization (a-CGH), is widely used today to screen for gains and losses in cancers and other genetic diseases with high resolution at the genome level or for specific chromosomal region. Statistical methods for analyzing such a-CGH data have been developed. However, most of the existing methods are for unrelated individual data and the results from them provide explanation for horizontal variations in copy number changes. It is potentially meaningful to develop a statistical method that will allow for the analysis of family data to investigate the vertical kinship effects as well. Here we consider a semiparametric model based on clustering method in which the marginal distributions are estimated nonparametrically, and the familial dependence structure is modeled by copula. The model is illustrated and evaluated using simulated data. Our results show that the proposed method is more robust than the commonly used multivariate normal model. Finally, we demonstrated the utility of our method using a real dataset.

Categories: Bioinformatics


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