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Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches we...
Source: BioData Mining - October 11, 2009 Category: Bioinformatics Authors: Vladimir Likic Source Type: journals

Spatially Uniform ReliefF (SURF) for Computationally-Efficient Filtering of Gene-Gene InteractionsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Conclusions: Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be used instead of ReliefF to filter a dataset before an exhaustive MDR analysis. This change increases the ability of a study to detect gene-gene interactions. The SURF algorithm is implemented in the open source Multifactor Dimensionality Reduction (MDR) software package available from www.epistasis.org. (Source: BioData Mining)
Source: BioData Mining - September 21, 2009 Category: Bioinformatics Authors: Casey GreeneNadia PenrodJeff KiralisJason Moore Source Type: journals

Partitioning clustering algorithms for protein sequence data setsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Background: Genome-sequencing projects are currently producing an enormous amount of new sequences and cause the rapid increasing of protein sequence databases. The unsupervised classification of these data into functional groups or families, clustering, has become one of the principal research objectives in structural and functional genomics. Computer programs to automatically and accurately classify sequences into families become a necessity. A significant number of methods have addressed the clustering of protein sequences and most of them can be categorized in three major groups: hierarchical, graph-based and partition...
Source: BioData Mining - April 2, 2009 Category: Bioinformatics Authors: Sondes Fayech, Nadia Essoussi and Mohamed Limam Source Type: journals

Multifactor dimensionality reduction analysis identifies specific nucleotide patterns promoting genetic polymorphismsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Background: The fidelity of DNA replication serves as the nidus for both genetic evolution and genomic instability fostering disease. Single nucleotide polymorphisms (SNPs) constitute greater than 80% of the genetic variation between individuals. A new theory regarding DNA replication fidelity has emerged in which selectivity is governed by base-pair geometry through interactions between the selected nucleotide, the complementary strand, and the polymerase active site. We hypothesize that specific nucleotide combinations in the flanking regions of SNP fragments are associated with mutation. Results: We modeled the relation...
Source: BioData Mining - March 30, 2009 Category: Bioinformatics Authors: Eric Arehart, Scott Gleim, Bill White, John Hwa and Jason H Moore Source Type: journals

A survey of visualization tools for biological network analysisEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing. (Source: BioData Mining)
Source: BioData Mining - November 28, 2008 Category: Bioinformatics Authors: Georgios A Pavlopoulos, Anna-Lynn Wegener and Reinhard Schneider Source Type: journals

Fast approximate hierarchical clustering using similarity heuristicsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Conclusion: The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust (Source: BioData Mining)
Source: BioData Mining - September 22, 2008 Category: Bioinformatics Authors: Meelis Kull and Jaak Vilo Source Type: journals

Filling the gap between biology and computer scienceEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful knowledge from heterogeneous biological data. We outline the aims and scope of the journal, introduce the publishing model and describe the open peer review policy, which fosters interaction within the research community. (Source: BioData Mining)
Source: BioData Mining - July 17, 2008 Category: Bioinformatics Authors: Jesús S Aguilar-Ruiz, Jason H Moore and Marylyn D Ritchie Source Type: journals

Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approachEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Conclusion: The proposed model provides a novel hypothesis on the genetic etiology of comorbid depression with AUD, consistent with established clinical and biochemical data. This analysis also provides an example of how an integrated bioinformatics approach can maximize the use of available biomedical data to improve our understanding of complex disease. (Source: BioData Mining)
Source: BioData Mining - July 17, 2008 Category: Bioinformatics Authors: Richard C McEachin, Benjamin J Keller, Erika FH Saunders and Melvin G McInnis Source Type: journals

Neural networks for genetic epidemiology: past, present, and futureEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex di...
Source: BioData Mining - July 17, 2008 Category: Bioinformatics Authors: Alison A Motsinger-Reif and Marylyn D Ritchie Source Type: journals

Uncovering mechanisms of transcriptional regulations by systematic mining of cis regulatory elements with gene expression profilesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Conclusion: The results validate that the CisTransMine approach is a robust method to uncover the hidden transcriptional regulatory mechanisms that can facilitate the discovery of mechanisms of transcriptional regulation. (Source: BioData Mining)
Source: BioData Mining - July 17, 2008 Category: Bioinformatics Authors: Qicheng Ma, Gung-Wei Chirn, Joseph D Szustakowski, Adel Bakhtiarova, Penelope A Kosinski, Daniel Kemp and Nanguneri Nirmala Source Type: journals

Clustering-based approaches to SAGE data miningEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation. (Source: BioData Mining)
Source: BioData Mining - July 17, 2008 Category: Bioinformatics Authors: Haiying Wang, Huiru Zheng and Francisco Azuaje Source Type: journals