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See detailOne consensual depression diagnosis tool to serve many countries: a challenge! A RAND/UCLA methodology
Nabbe, P.; Le Reste, J. Y.; Guillou-Landreat, M. et al

in BMC Research Notes (2018), 11(1), 4

From a systematic literature review (SLR), it became clear that a consensually validated tool was needed by European General Practitioner (GP) researchers in order to allow multi-centred collaborative ... [more ▼]

From a systematic literature review (SLR), it became clear that a consensually validated tool was needed by European General Practitioner (GP) researchers in order to allow multi-centred collaborative research, in daily practice, throughout Europe. Which diagnostic tool for depression, validated against psychiatric examination according to the DSM, would GPs select as the best for use in clinical research, taking into account the combination of effectiveness, reliability and ergonomics? A RAND/UCLA, which combines the qualities of the Delphi process and of the nominal group, was used. GP researchers from different European countries were selected. The SLR extracted tools were validated against the DSM. The Youden index was used as an effectiveness criterion and Cronbach's alpha as a reliability criterion. Ergonomics data were extracted from the literature. Ergonomics were tested face-to-face. [less ▲]

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See detailHuman gene correlation analysis (HGCA): A tool for the identification of transcriptionally co-expressed genes
Michalopoulos, Ioannis; Pavlopoulos, Georgios; Malatras, Apostolos et al

in BMC Research Notes (2012), 5(265), 1-11

Background: Bioinformatics and high-throughput technologies such as microarray studies allow the measure of the expression levels of large numbers of genes simultaneously, thus helping us to understand ... [more ▼]

Background: Bioinformatics and high-throughput technologies such as microarray studies allow the measure of the expression levels of large numbers of genes simultaneously, thus helping us to understand the molecular mechanisms of various biological processes in a cell. Findings: We calculate the Pearson Correlation Coefficient (r-value) between probe set signal values from Affymetrix Human Genome Microarray samples and cluster the human genes according to the r-value correlation matrix using the Neighbour Joining (NJ) clustering method. A hyper-geometric distribution is applied on the text annotations of the probe sets to quantify the term overrepresentations. The aim of the tool is the identification of closely correlated genes for a given gene of interest and/or the prediction of its biological function, which is based on the annotations of the respective gene cluster. Conclusion: Human Gene Correlation Analysis (HGCA) is a tool to classify human genes according to their coexpression levels and to identify overrepresented annotation terms in correlated gene groups. It is available at: http://biobank-informatics.bioacademy.gr/coexpression/. [less ▲]

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See detailMedusa: A tool for exploring and clustering biological networks.
Pavlopoulos, Georgios A.; Hooper, S. D.; Sifrim, A. et al

in BMC Research Notes (2011), 4(1), 384

Background: Biological processes such as metabolic pathways, gene regulation or protein-protein interactions are often represented as graphs in systems biology. The understanding of such networks, their ... [more ▼]

Background: Biological processes such as metabolic pathways, gene regulation or protein-protein interactions are often represented as graphs in systems biology. The understanding of such networks, their analysis, and their visualization are today important challenges in life sciences. While a great variety of visualization tools that try to address most of these challenges already exists, only few of them succeed to bridge the gap between visualization and network analysis. Findings: Medusa is a powerful tool for visualization and clustering analysis of large-scale biological networks. It is highly interactive and it supports weighted and unweighted multi-edged directed and undirected graphs. It combines a variety of layouts and clustering methods for comprehensive views and advanced data analysis. Its main purpose is to integrate visualization and analysis of heterogeneous data from different sources into a single network. Conclusions: Medusa provides a concise visual tool, which is helpful for network analysis and interpretation. Medusa is offered both as a standalone application and as an applet written in Java. It can be found at: https:// sites.google.com/site/medusa3visualization. [less ▲]

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See detailWhich clustering algorithm is better for predicting protein complexes?
Moschopoulos, Charalampos N.; Pavlopoulos, Georgios A.; Iacucci, Ernesto et al

in BMC Research Notes (2011), (4), 549

Background Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the ... [more ▼]

Background Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks. Results In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases. Conclusions While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm [less ▲]

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See detailComparative analysis of miRNAs and their targets across four plant species.
Lenz, Dorina; May, Patrick UL; Walther, Dirk

in BMC Research Notes (2011), 4

BACKGROUND: MicroRNA (miRNA) mediated regulation of gene expression has been recognized as a major posttranscriptional regulatory mechanism also in plants. We performed a comparative analysis of miRNAs ... [more ▼]

BACKGROUND: MicroRNA (miRNA) mediated regulation of gene expression has been recognized as a major posttranscriptional regulatory mechanism also in plants. We performed a comparative analysis of miRNAs and their respective gene targets across four plant species: Arabidopsis thaliana (Ath), Medicago truncatula(Mtr), Brassica napus (Bna), and Chlamydomonas reinhardtii (Cre). RESULTS: miRNAs were obtained from mirBase with 218 miRNAs for Ath, 375 for Mtr, 46 for Bna, and 73 for Cre, annotated for each species respectively. miRNA targets were obtained from available database annotations, bioinformatic predictions using RNAhybrid as well as predicted from an analysis of mRNA degradation products (degradome sequencing) aimed at identifying miRNA cleavage products. On average, and considering both experimental and bioinformatic predictions together, every miRNA was associated with about 46 unique gene transcripts with considerably variation across species. We observed a positive and linear correlation between the number miRNAs and the total number of transcripts across different plant species suggesting that the repertoire of miRNAs correlates with the size of the transcriptome of an organism. Conserved miRNA-target pairs were found to be associated with developmental processes and transcriptional regulation, while species-specific (in particular, Ath) pairs are involved in signal transduction and response to stress processes. Conserved miRNAs have more targets and higher expression values than non-conserved miRNAs. We found evidence for a conservation of not only the sequence of miRNAs, but their expression levels as well. CONCLUSIONS: Our results support the notion of a high birth and death rate of miRNAs and that miRNAs serve many species specific functions, while conserved miRNA are related mainly to developmental processes and transcriptional regulation with conservation operating at both the sequence and expression level. [less ▲]

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See detailAdvanced spot quality analysis in two-colour microarray experiments
Yatskou, M.; Novikov, E.; Vetter, G. et al

in BMC Research Notes (2008), 1

Background: Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of ... [more ▼]

Background: Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information. Findings: We evaluated the performance of two image analysis packages MAIA and GenePix (GP) using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5%) than GP with default spot filtering conditions. Conclusion: Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions. © 2008 Friederich et al; licensee BioMed Central Ltd. [less ▲]

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