Biological data integration: wrapping data and tools

Lacroix, Zoé

doi:10.1109/titb.2002.1006299

Cited by 40 publications

(17 citation statements)

References 22 publications

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“…Thus, despite their wide availability, these data are not generally machine-searchable. Consequently, no promising or significant increase in the efficiency of data integration may be expected, for example, from the automation of PubMed data retrieval (Teufel et al 2006;Lacroix 2002;Colhoun 2003). Regarding GWASs, no publicly available database is currently storing the enormous amount of accumulating data.…”

Section: Obstacles In Meta-analysismentioning

confidence: 99%

Trends in meta-analysis of genetic association studies

Ζιντζαράς

Lau

2007

J Hum Genet

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The number of published genetic association studies (GASs) is increasing tremendously due to the availability of mapped single-nucleotide polymorphisms (SNPs) and advances in genotyping technologies. A search in HuGENet illustrates the rapid accumulation of evidence for major diseases. Recently, there has been a lot of activity regarding genome-wide association studies (GWASs), and a growing number of forthcoming studies is expected. GASs and GWASs are usually underpowered to detect significant associations, and the varying quality of reporting publications befuddles researchers. A meta-analysis can increase power and provide standards of reporting results. However, the conduct of a meta-analysis of GASs faces a major obstacle, which is the structure and diversity of stored information in databases. Similar problems are expected for GWASs, though the data are not yet publicly available. The development of a Web-based system for the detailed and structured recording of GAS or GWAS data, accompanied by an estimation of the overall genetic risk effects, would enable scientists to keep track of evidence for gene-disease associations.

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Section: Obstacles In Meta-analysismentioning

confidence: 99%

Trends in meta-analysis of genetic association studies

Ζιντζαράς

Lau

2007

J Hum Genet

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“…The proposed technique imposes regional intensity constraints on the dilationgenerated maxima so as to cope with the presence of noise and artifacts. Furthermore, it is automated, efficient, and consistent with the time-consuming work of biologists [20], [21]. As such, it extends the preliminary 2-D PAGE image analysis approach proposed in [22].…”

Section: Introductionmentioning

confidence: 75%

A Computer-Based Technique for Automated Spot Detection in Proteomics Images

Mylona

Savelonas

Maroulis

et al. 2011

IEEE Trans. Inform. Technol. Biomed.

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Abstract-This paper introduces a novel computer-based technique for automated detection of protein spots in proteomics images. The proposed technique is based on the localization of regional intensity maxima associated with protein spots and is formulated so as to ignore rectangular-shaped streaks, minimize the detection of false negatives, and allow the detection of multiple overlapping spots. Regional intensity constraints are imposed on the localized maxima in order to cope with the presence of noise and artifacts. The experimental evaluation of the proposed technique on real proteomics images demonstrates that it: 1) achieves a predictive value (PV) and detection sensitivity (DS) which exceed 90%; 2) outperforms Melanie software package in terms of PV, specificity, and DS; 3) ignores artifacts; 4) distinguishes multiple overlapping spots; 5) locates spots within streaks; and 6) is automated and efficient.

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“…Thus, despite their wide availability, these data are not generally machine-readable. Consequently, no promising, significant increase in the efficiency of data integration may be expected from automation of Pubmed data retrieval (33,34).…”

Section: Future Prospects; Data Integration and Gene Ontologymentioning

confidence: 99%

Current bioinformatics tools in genomic biomedical research (Review)

Teufel

Krupp²,

Weinmann³

et al. 2006

Int J Mol Med

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Abstract. On the advent of a completely assembled human genome, modern biology and molecular medicine stepped into an era of increasingly rich sequence database information and high-throughput genomic analysis. However, as sequence entries in the major genomic databases currently rise exponentially, the gap between available, deposited sequence data and analysis by means of conventional molecular biology is rapidly widening, making new approaches of high-throughput genomic analysis necessary. At present, the only effective way to keep abreast of the dramatic increase in sequence and related information is to apply biocomputational approaches. Thus, over recent years, the field of bioinformatics has rapidly developed into an essential aid for genomic data analysis and powerful bioinformatics tools have been developed, many of them publicly available through the World Wide Web. In this review, we summarize and describe the basic bioinformatics tools for genomic research such as: genomic databases, genome browsers, tools for sequence alignment, single nucleotide polymorphism (SNP) databases, tools for ab initio gene prediction, expression databases, and algorithms for promoter prediction.

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Biological data integration: wrapping data and tools

Cited by 40 publications

References 22 publications

Trends in meta-analysis of genetic association studies

Trends in meta-analysis of genetic association studies

A Computer-Based Technique for Automated Spot Detection in Proteomics Images

Current bioinformatics tools in genomic biomedical research (Review)

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