Samuele E. Locatelli
36 lines
2.1 KiB
Plaintext
36 lines
2.1 KiB
Plaintext
<%@ Control Language="C#" AutoEventWireup="true" CodeBehind="mod_about.ascx.cs" Inherits="WebLCP.WUC.mod_about" %>
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<h2>PROJECT DESCRIPTION</h2>
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<img src="../Images/brain.png" class="img-fluid" width="128" />
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<p>The project aims at detecting the presence of any cut-offs in AD biomarkers distribution that allows to classify subject/patients in terms of prognostic evaluation. In particular, the main target is to provide a free, user-friendly on-line interface of an ad-hoc developed algorithm that evaluates the effects of genetic/demographic AD-related factors (APOE, age and sex) on the detected biomarker cut-offs. The idea is to allow people non-familiar with the R package to use this powerful instrument for improving the disease understanding and to create a platform for collaboration of different centers and researchers.</p>
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This on-line software is an open and user-friendly tool for:
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<li>extracting any data-driven cut-off of quantitative biomarker (i.e. continuous variable) and
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<li>evaluating the effect of APOE, age and sex on cut-offs estimation (Marizzoni, Ferrari, et al. 2019; <a href="https://doi.org/10.1016/j.neurobiolaging.2019.12.019" target="_blank">https://doi.org/10.1016/j.neurobiolaging.2019.12.019</a>).
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The cut-off extraction is based on finite Gaussian mixture model approach that allows to produce a probabilistic clustering/classification of the data into any subgroups (defined by the components of the mixture distribution) of the overall population. The effect of APOE, age and sex on cut-offs values was estimated by applying an extension of finite mixture model applied to a generalized linear model (more details are reported
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<asp:HyperLink runat="server" NavigateUrl="~/MethodSpecs">here.</asp:HyperLink>)
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