Additional file 1: of Effects of pre-notification, invitation length, questionnaire length and reminder on participation rate: a quasi-randomised controlled trial

English translation of the invitation letters as well as the checklist. A. Long version of the invitation letter. B. Short version of the invitation letter. (PDF 184 kb

Graphical illustration of the external predictive ability of proteochemometric models for HIV-1 protease drug susceptibility

Data for one inhibitor at a time were excluded from the dataset and predicted from proteochemometric models built on the remaining data. The predicted versus measured susceptibility values for indinavir (A) and saquinavir (B) are shown. Goodness-of-fit of the models (i.e. model data) are shown as light gray symbols in panels A and B.<p><b>Copyright information:</b></p><p>Taken from "Proteochemometric modeling of HIV protease susceptibility"</p><p>http://www.biomedcentral.com/1471-2105/9/181</p><p>BMC Bioinformatics 2008;9():181-181.</p><p>Published online 10 Apr 2008</p><p>PMCID:PMC2375133.</p><p></p

Changes in the susceptibility to the seven inhibitors due to single point mutations in the wild-type HIV-1 protease

Shown are the decimal logarithms of the fold-decreases in susceptibility (FDS) calculated from the proteochemometric model.<p><b>Copyright information:</b></p><p>Taken from "Proteochemometric modeling of HIV protease susceptibility"</p><p>http://www.biomedcentral.com/1471-2105/9/181</p><p>BMC Bioinformatics 2008;9():181-181.</p><p>Published online 10 Apr 2008</p><p>PMCID:PMC2375133.</p><p></p

Screenshot from the Web service for the proteochemometric susceptibility model of HIV protease inhibitors

The publicly available prediction service takes an HIV protease sequence as input and predicts its susceptibility to seven protease inhibitors using the proteochemometric model. The output is graphical and indicates any anomalies in the submitted sequence with respect to the data in the model. Shown are results for a protease with the quadruple mutation 24I, 46L, 54V, and 82A. The Web service can be found at [22].<p><b>Copyright information:</b></p><p>Taken from "Proteochemometric modeling of HIV protease susceptibility"</p><p>http://www.biomedcentral.com/1471-2105/9/181</p><p>BMC Bioinformatics 2008;9():181-181.</p><p>Published online 10 Apr 2008</p><p>PMCID:PMC2375133.</p><p></p

Prostate cancer (PC) knowledge by participation to risk-based PC screening, three months before invitation to screening.

<p>Prostate cancer (PC) knowledge by participation to risk-based PC screening, three months before invitation to screening.</p

Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines

QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: <i>C</i>, γ, and signature height. <i>C</i> is a penalty parameter that limits overfitting, γ controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using <i>C</i> in the range of 1 to 100 and γ in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected

Men’s worry and perceived vulnerability to prostate cancer (PC) by participation to risk-based PC screening, three months before invitation to screening.

<p>Men’s worry and perceived vulnerability to prostate cancer (PC) by participation to risk-based PC screening, three months before invitation to screening.</p

Health behaviour scale scores by participation to risk-based PC screening, three months before invitation to screening.

<p>Health behaviour scale scores by participation to risk-based PC screening, three months before invitation to screening.</p

Ligand-Based Target Prediction with Signature Fingerprints

When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power

Additional file 2: of Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density

Tables S3-S6, S8, S9 and Figures S1-S7 Additional material and methods description, results and detailed discussion. (PDF 2026 kb