Publications by authors named "D Diacono"
Article Synopsis
- - Advances in DNA sequencing have transformed plant genomics, but predicting plant traits (phenotypes) from genetic data is still difficult, especially in breeding contexts; this study aims to improve prediction accuracy by using explainable AI with machine learning.
- - The research compared various machine learning methods to predict the almond shelling fraction using data from an almond collection, revealing that the Random Forest method provided the best predictions and identified important genetic regions linked to the trait.
- - The study demonstrated that explainable AI not only improves the understanding of genetic factors related to phenotypes but also plays a crucial role in enhancing crop production in sustainable agriculture.
Autism spectrum disorder (ASD) affects social interaction and communication. Emerging evidence links ASD to gut microbiome alterations, suggesting that microbial composition may play a role in the disorder. This study employs explainable artificial intelligence (XAI) to examine the contributions of individual microbial species to ASD.
View Article and Find Full Text PDFIdentifying the origin of a food product holds paramount importance in ensuring food safety, quality, and authenticity. Knowing where a food item comes from provides crucial information about its production methods, handling practices, and potential exposure to contaminants. Machine learning techniques play a pivotal role in this process by enabling the analysis of complex data sets to uncover patterns and associations that can reveal the geographical source of a food item.
View Article and Find Full Text PDFRespiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data.
View Article and Find Full Text PDFBackground: Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods.
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