Nicola Rares Franco
Ghent University Hospital
ParametrizationDeep learningMachine learningCancerBasis (linear algebra)Internal medicineBenchmark (computing)Dimension (vector space)OncologyLogistic regressionSingle-nucleotide polymorphismArtificial intelligenceNonlinear systemRadiogenomicsSet (abstract data type)ManifoldProspective cohort studyExternal beam radiotherapyNocturiaConstructiveProstate cancerTask (project management)Applied mathematicsPopulationToxicityComputer scienceArtificial neural networkCategorical variableDuality (mathematics)Receiver operating characteristicBinary classificationMedicineCohortUrinary systemSelection (genetic algorithm)Pattern searchGastroenterologyPartial differential equation
Publications 5
#1Nicola Rares Franco (Ghent University Hospital)H-Index: 1
#1Nicola Rares Franco (Ghent University Hospital)
Last. Tiziana RancatiH-Index: 27
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AIM To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). MATERIALS AND METHODS Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 li...
#2Andrea ManzoniH-Index: 22
Last. Paolo ZuninoH-Index: 28
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Within the framework of parameter dependent PDEs, we develop a constructive approach based on Deep Neural Networks for the efficient approximation of the parameter-to-solution map. The research is motivated by the limitations and drawbacks of state-of-the-art algorithms, such as the Reduced Basis method, when addressing problems that show a slow decay in the Kolmogorov n-width. Our work is based on the use of deep autoencoders, which we employ for encoding and decoding a high fidelity approximat...
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
Last. Paolo ZuninoH-Index: 28
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Logistic Regression (LR) is a widely used statistical method in empirical binary classification studies. However, real-life scenarios oftentimes share complexities that prevent from the use of the as-is LR model, and instead highlight the need to include high-order interactions to capture data variability. This becomes even more challenging because of: (i) datasets growing wider, with more and more variables; (ii) studies being typically conducted in strongly imbalanced settings; (iii) samples g...
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
#2Francesca Gasperoni (University of Cambridge)H-Index: 1
Last. Tiziana RancatiH-Index: 27
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Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors....
3 CitationsSource
Last. Catharine M L WestH-Index: 77
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