Publications by authors named "P VITALE"
: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV failure is crucial to prevent adverse outcomes. This study aims to identify predictors of first-attempt NIV failure in PICU patients by testing various machine learning techniques and comparing their predictive abilities.
View Article and Find Full Text PDFBackground: Squamous cell carcinoma of the head and neck (SCCHN) accounts for 3% of all malignant tumors in Italy. Immune checkpoint inhibitors combined with chemotherapy is first-line treatment for SCCHN; however, second-line treatment options are limited. Taxanes are widely used for combination therapy of SCCHN, as clinical trials have shown their efficacy in patients with this disease, particularly in patients with prior therapy.
View Article and Find Full Text PDFArticle Synopsis
- Genomic prediction (GP) in durum wheat has shown mixed results, with multivariate (MV) analysis emerging as a promising approach to enhance prediction accuracy (PA) for certain traits.
- The study assessed PA of agronomic traits over two seasons and varying field conditions (high nitrogen/well-watered and low nitrogen/rainfed), applying univariate (UV) and multivariate models with different cross-validation schemes (MV-CV1 and MV-CV2).
- Results indicated that MV-CV2 significantly improved PA, with some traits experiencing increases of up to 56.72%, especially when modeling related traits together, highlighting the potential of multivariate approaches in genomic prediction.
In plant breeding, Multi-Environment Trials (METs) evaluate candidate genotypes across various conditions, which is financially costly due to extensive field testing. Sparse testing addresses this challenge by evaluating some genotypes in selected environments, allowing for a broader range of environments without significantly increasing costs. This approach integrates genomic information to adjust phenotypic data, leading to more accurate genetic effect estimations.
View Article and Find Full Text PDFArticle Synopsis
- Statistical machine learning (ML) analyzes large volumes of genomic, phenotypic, and environmental data to uncover patterns and improve prediction models in plant breeding.
- By investigating genotype-by-environment (G×E) interactions, ML helps identify genetic factors that influence performance in various environments.
- This review emphasizes how big data and ML enhance prediction accuracy and streamline breeding strategies through comprehensive analysis of diverse datasets.