Giovanna Dimitri

• PhD in Computer Science
• University of Cambridge

Despite the growing popularity of organic foods, research on their effects on human health, particularly regarding cancer and diabetes, remains limited. While some studies suggest potential health benefits, others yield conflicting results or lack sufficient evidence to draw conclusions. Understanding the causal relationship between organic food co...

In the agri-food sector, traceability is essential to ensure the quality, safety, and transparency of supply chains, where transportation companies are key stakeholders in the overall movement of goods. The multitude of actors involved in supply chains makes it challenging to achieve the above mentioned objectives: each company usually uses its own...

This work describes the ongoing design and development of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like acce...

In today’s business landscape, Chatbots play a pivotal role in innovation and process optimization. In this paper, we introduced a novel advanced Emotional Chatbot AI, introducing sentiment analysis for human chatbot conversations. Adding an emotional component within the human-computer interaction, can in fact dramatically improve the quality of t...

In this work we present the use of Semantic Based Regularization Kernel based machine learning method to predict protein function. We initially build the protein functions ontology, given an initial list of proteins. We subsequently performed predictions, both at individual and at joint levels of functions, introducing and adding to the learning pr...

In this manuscript we will present a brief overview of the comorbidity concept. We will start by laying its foundations and its definitions and then describing the role that machine learning can hold in mining and defining it. The purpose of this short survey is to present a brief overview of the definition of comorbidity as a concept, and showing...

Mechanotransduction is the process that enables the conversion of mechanical cues into biochemical signaling. While all our cells are well known to be sensitive to such stimuli, the details of the systemic interaction between mechanical input and inflammation are not always well integrated. Often, they are considered and studied in relatively compa...

The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors wit...

Artificial Intelligence (AI) applications and Machine Learning (ML) methods have gained much attention in recent years for their ability to automatically detect patterns in data without being explicitly taught rules. Specific features characterise the ECGs of patients with Brugada Syndrome (BrS); however, there is still ambiguity regarding the corr...

In this study, we introduce an innovative application of clustering algorithms to assess and appraise Italy’s alignment with respect to the Sustainable Development Goals (SDGs), focusing on those related to climate change and the agrifood market. Specifically, we examined SDG 02: Zero Hunger, SDG 12: Responsible Consumption and Production, and SDG...

Image geolocalization is receiving increasing attention due to its importance in several applications, such as image retrieval, criminal investigations and fact-checking. Previous works focused on several instances of image geolocalization including place recognition, GPS coordinates estimation and country recognition. In this paper, we tackle an e...

The Agritech project is a large Italian project funded with the National Recovery and Resilience Plan to contribute, among other objectives, to the digitization of Agriculture in Italy and to the implementation of an information technology platform and its web-based abstraction (called Web of Agriculture-WoA) that will support research, production...

Vaccine hesitancy, or the reluctance to be vaccinated, is a phenomenon that has recently become particularly significant, in conjunction with the vaccination campaign against COVID-19. During the lockdown period, necessary to control the spread of the virus, social networks have played an important role in the Italian debate on vaccination, general...

The highly realistic image quality achieved by current image generative models has many academic and industrial applications. To limit the use of such models to benign applications, though, it is necessary that tools to conclusively detect whether an image has been generated synthetically or not are developed. For this reason, several detectors hav...

In 2021 almost 300 mm of rain, nearly half of the average annual rainfall, fell near Catania (Sicily Island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. These phenomena are now very common in various countries all around the world: this is t...

Recently, it has become progressively more evident that classic diagnostic labels are unable to reliably describe the complexity and variability of several clinical phenotypes. This is particularly true for a broad range of neuropsychiatric illnesses (e.g., depression, anxiety disorders, behavioral phenotypes). Patient heterogeneity can be better d...

The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a significant increase in both theoretical and applicative studies. On the one hand, the versatility and the ability to tackle complex tasks have led to the rapid and widespread diffusion of DL technologies. On the other hand, the dizzying increase in...

If understanding sentiments is already a difficult task in human‐human communication, this becomes extremely challenging when a human‐computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network‐based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot v...

The automotive market is experiencing, in recent years, a period of deep transformation. Increasingly stricter rules on pollutant emissions and greater awareness of air quality by consumers are pushing the transport sector towards sustainable mobility. In this historical context, electric cars have been considered the most valid alternative to trad...

The analysis of social networks, can lead to important discoveries concerning society and trends. Can in fact imply the discovery of several new aspects of social behavior, as well as understanding the interest behind certain topics. Facebook, is now used worldwide, by approximately 3 billions of users, and has become one of the main sources of inf...

Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, can provide HR departments with a valuable decision support system and, as a...

Deep learning has achieved state-of-the-art performances in several research applications nowadays: from computer vision to bioinformatics, from object detection to image generation. In the context of such newly developed deep-learning approaches, we can define the concept of multimodality. The objective of this research field is to implement metho...

Biological aging can be affected by several factors such as drug treatments and pathological conditions. Metabolomics can help in the estimation of biological age by analyzing the differences between predicted and actual chronological age in different subjects. In this paper, we compared three different and well-known machine learning approaches—SV...

Predicting the country where a picture has been taken from has many potential applications, like detection of false claims, impostors identification, prevention of disinformation campaigns, identification of fake news and so on. Previous works have focused mostly on the estimation of the geo-coordinates where a picture has been taken. Yet, recogniz...

Recently, it has become progressively more evident that classic diagnostic labels are unable to accurately and reliably describe the complexity and variability of several clinical phenotypes. This is particularly true for a broad range of neuropsychiatric illnesses such as depression and anxiety disorders or behavioural phenotypes such as aggressio...

Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decisio...

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This research project is funded by Tuscany Region Background/Introduction Electrocardiograms (ECGs) are rapidly moving from analog to digital versions. Consequently, a series of automatic analyses of standard 12-lead ECGs are attractin...

Drug Side--Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side--effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in ti...

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time...

Parkinson’s disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil...

Advanced age represents one of the major risk factors for Parkinson's Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson's Disease, has not been fully elucidated. Therefore, the aim of the present study is to unravel...

Eye-tracking can offer a novel clinical practice and a non-invasive tool to detect neuropathological syndromes. In this paper, we show some analysis on data obtained from the visual sequential search test. Indeed, such a test can be used to evaluate the capacity of looking at objects in a specific order, and its successful execution requires the op...

Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the comm...

A prodromal phase of Parkinson’s disease (PD) may precede motor manifestations by decades. PD patients’ siblings are at higher risk for PD, but the prevalence and distribution of prodromal symptoms are unknown. The study objectives were (1) to assess motor and non-motor features estimating prodromal PD probability in PD siblings recruited within th...

The automatic segmentation of the aorta can be extremely useful in clinical practice, allowing the diagnosis of numerous pathologies to be sped up, such as aneurysms and dissections, and allowing rapid reconstructive surgery, essential in saving patients’ lives. In recent years, the success of Deep Learning (DL)-based decision support systems has i...

Background: Traumatic brain injury (TBI) is an extremely heterogeneous and complex pathology that requires the integration of different physiological measurements for the optimal understanding and clinical management of patients. Information derived from intracranial pressure (ICP) monitoring can be coupled with information obtained from heart rat...

Objective: In a previous study, we observed the presence of simultaneous increases in intracranial pressure (ICP) and the heart rate (HR), which we denominated cardio-cerebral crosstalk (CC), and we related the number of such events to patient outcomes in a paediatric cohort. In this chapter, we present an extension of this work to an adult cohort...

Recently, it has become progressively more evident that classic diagnostic labels are unable to accurately and reliably describe the complexity and variability of several clinical phenotypes. This is particularly true for a broad range of neuropsychiatric illnesses such as depression and anxiety disorders or behavioural phenotypes such as aggressio...

Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of standardized data, information and methodologies to assess disease severity and progression represents a common complication in ultra-rare disorders like AKU. This is the reason why we developed a compre...

Advanced age is the major risk factor for idiopathic Parkinson's disease (PD), but to date the biological relationship between PD and ageing remains elusive. Here we describe the rationale and the design of the H2020 funded project "PROPAG-AGEING", whose aim is to characterize the contribution of the ageing process to PD development. We summarize c...

Advanced age is the major risk factor for idiopathic Parkinson’s disease (PD), but to date the biological relationship between PD and ageing remains elusive. Here we describe the rationale and the design of the H2020 funded project “PROPAG-AGEING”, whose aim is to characterize the contribution of the ageing process to PD development. We summarize c...

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain useful for patients, families, and their access to dedicated NHS and health care services, the traditio...

Here, we performed a comprehensive intra-tissue and inter-tissue network analysis of the human transcriptome. We generated an atlas of communities in co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (represe...

Functional MRI (fMRI) attracts huge interest for the machine learning community nowadays. In this work we propose a novel data augmentation procedure through analysing the inherent noise in fMRI. We then use the novel augmented dataset for the classification of subjects by age and gender, showing a significant improvement in the accuracy performanc...

The term neuroscience includes in itself a plethora of research areas devoted to undercover the most fascinating complex organ of our body: the brain. A common denominator of neuroscience areas, is the need for the application of methodologies to integrate different features. In this thesis, we focused on the analysis of two types of brain data: br...

The genetic component of many common traits is associated with the gene expression and several variants act as expression quantitative loci, regulating the gene expression in a tissue specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction models on the genotype of 808 samples including controls, subjects with...

Alkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2‐dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum...

Graphs are a natural choice to encode data in many real–world applications. In fact, a graph can describe a given pattern as a complex structure made up of parts (the nodes) and relationships between them (the edges). Despite their rich representational power, most of machine learning approaches cannot deal directly with inputs encoded by graphs. I...

Depression, as it stands, is the third leading contributor to global diseases – whose early detection can only help prevent various global diseases in the future. Researchers, also suggest that depression boosts alcoholic abuse and alcohol abuse can also add to the tendency towards depression. In this research paper, alcoholic patients as well as c...

Learning machines for pattern recognition, such as neural networks or support vector machines, are usually conceived to process real–valued vectors with predefined dimensionality even if, in many real–world applications, relevant information is inherently organized into entities and relationships between them. Instead, Graph Neural Networks (GNNs)...

Objectives: The detection of increasing intracranial pressure (ICP) is important in preventing secondary brain injuries. Before mean ICP increases critically, transient ICP elevations may be observed. We have observed ICP transients of less than 10 min duration ,which occurred simultaneously with transient increases in heart rate (HR). These simul...

Background We present a multiplex network model for the analysis of Intracranial Pressure (ICP) and Heart Rate (HR) behaviour after severe brain traumatic injuries in pediatric patients. The ICP monitoring is of vital importance for checking life threathening conditions, and understanding the behaviour of these parameters is crucial for a successfu...

Background: Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predictions, are crucial for understanding side effects. Methods: In this paper we present DrugClust,...

Background: Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predictions, are crucial for understanding side effects. Methods: In this paper we present DrugClust, a m...