Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination

Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets

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

Predicting incidence and overdiagnosis rate ratios for 2-yearly and 8-yearly screening between 55 and 69 years of age and the cessation of asymptomatic testing compared with current testing uptake.

The changes in testing policy were introduced in 2015 for a population reflecting the Swedish age-structure.</p

Modelled current PSA testing rates per person-year for ages 40-80 years and the calendar period 1995-2014 for men without an existing prostate cancer diagnosis.

The white contour lines indicates the rates 0.1, 0.2 and 0.3. The modelled values are based on data from the Stockholm PSA and Biopsy Register [15].</p

QLQ-C30 scale scores by participation to risk-based PC screening, three months before invitation to screening.

QLQ-C30 scale scores by participation to risk-based PC screening, three months before invitation to screening.</p

Comparing the modelled proportion of Gleason scores at cancer onset from FHCRC model in 2013 and 2018 with the Stockholm Prostata model.

Comparing the modelled proportion of Gleason scores at cancer onset from FHCRC model in 2013 and 2018 with the Stockholm Prostata model.</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

Overview of data sources and their linkage.

Overview of data sources and their linkage.</p