On the persistence of supplementary resources in biomedical publications

Background  

Providing for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers.     

Identification of biomolecule mass transport and binding rate parameters in living cells by inverse modeling

Background  

Quantification of in-vivo biomolecule mass transport and reaction rate parameters from experimental data obtained by Fluorescence Recovery after Photobleaching (FRAP) is becoming more important.     

A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments

Background  

With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across multiple studies.     

Providing visualisation support for the analysis of anatomy ontology data

Background  

Improvements in technology have been accompanied by the generation of large amounts of complex data. This same technology must be harnessed effectively if the knowledge stored within the data is to be retrieved. Storing data in ontologies aids its management; ontologies serve as controlled vocabularies that promote data exchange and re-use, improving analysis.     

The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data

Background  

The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets.     

Kernel-based distance metric learning for microarray data classification

Background  

The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample size, which make the task quite challenging.     

Relative quantification of mRNA: comparison of methods currently used for real-time PCR data analysis

Background  

Fluorescent data obtained from real-time PCR must be processed by some method of data analysis to obtain the relative quantity of target mRNA. The method chosen for data analysis can strongly influence results of the quantification.     

Time-dependent structural transformation analysis to high-level Petri net model with active state transition diagram

Background  

With an accumulation of in silico data obtained by simulating large-scale biological networks, a new interest of research is emerging for elucidating how living organism functions over time in cells.     

Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models

Background  

With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups.     

Probabilistic principal component analysis for metabolomic data

Background  

Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model.     

Willows: a memory efficient tree and forest construction package

Background  

Existing tree and forest methods are powerful bioinformatics tools to explore high dimensional data including high throughput genomic data. However, they cannot deal with the data generated by recent genotyping platforms for single nucleotide polymorphisms due to the massive size of the data and its excessive memory demand.     

ANMM4CBR: a case-based reasoning method for gene expression data classification

Background  

Accurate classification of microarray data is critical for successful clinical diagnosis and treatment. The "curse of dimensionality" problem and noise in the data, however, undermines the performance of many algorithms.     

Identification of biomarkers from mass spectrometry data using a "common" peak approach

Background  

Proteomic data obtained from mass spectrometry have attracted great interest for the detection of early-stage cancer. However, as mass spectrometry data are high-dimensional, identification of biomarkers is a key problem.     

STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data

Background  

Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult.     

TabSQL: a MySQL tool to facilitate mapping user data to public databases

Background  

With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases.     

The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications

Background  

Information resources on the World Wide Web play an indispensable role in modern biology. But integrating data from multiple sources is often encumbered by the need to reformat data files, convert between naming systems, or perform ongoing maintenance of local copies of public databases. Opportunities for new ways of combining and re-using data are arising as a result of the increasing use of web protocols to transmit structured data.     

GenoLink: a graph-based querying and browsing system for investigating the function of genes and proteins

Background  

A large variety of biological data can be represented by graphs. These graphs can be constructed from heterogeneous data coming from genomic and post-genomic technologies, but there is still need for tools aiming at exploring and analysing such graphs. This paper describes GenoLink, a software platform for the graphical querying and exploration of graphs.     

An eScience-Bayes strategy for analyzing omics data

Background  

The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems.