Marco Salvatore Nobile

Marco Salvatore Nobile
Ca' Foscari University of Venice | UNIVE · Department of Environmental Sciences, Informatics and Statistics

PhD in Computer Science

About

137
Publications
31,928
Reads
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1,773
Citations
Introduction
Associate Professor of Computer Science with the Ca' Foscari University of Venice (Italy). My research focuses on the development of novel Computational Intelligence methods (evolutionary computation, swarm intelligence, fuzzy logic, neural networks) applied to complex problems in computational and systems biology. Due to the massive computational requirements of such methods, I also work on the acceleration of algorithms by means of high performance architectures, GPUs in particular.
Additional affiliations
August 2019 - present
Eindhoven University of Technology
Position
  • Professor (Assistant)
Education
January 2012 - February 2015
Università degli Studi di Milano-Bicocca
Field of study
  • Computer Science

Publications

Publications (137)
Article
Full-text available
Motivation: The elucidation of dysfunctional cellular processes that can induce the onset of a disease is a challenging issue from both experimental and computational perspectives. Here we introduce a novel computational method based on the coupling between fuzzy logic modeling and a global optimization algorithm, whose aims are to (1) predict the...
Article
Full-text available
The investigation of cell proliferation can provide useful insights for the comprehension of cancer progression, resistance to chemotherapy and relapse. To this aim, computational methods and experimental measurements based on in vivo label-retaining assays can be coupled to explore the dynamic behavior of tumoral cells. ProCell is a software tha...
Preprint
Full-text available
Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanist...
Article
Among the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective methods for non-linear and complex high-dimensional problems. Since PSO performance strongly depends on the choice of its settings (i.e., inertia, cognitive and social factors, minimum and maximum velocity), Fuzzy Logic (FL) was previou...
Article
Full-text available
During long-distance migrations, some bird species make use of in-wake flying, which should allow them to profit from the upwash produced by another bird. While indirect evidence supports energy saving as the primary benefit of in-wake flying, measurements are still missing. We equipped migrating northern bald ibises (Geronticus eremita) with high-...
Article
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Fuzzy logic is useful tool to describe and represent biological or medical scenarios, where often states and outcomes are not only completely true or completely false, but rather partially true or partially false. Despite its usefulness and spread, fuzzy logic modeling might easily be done in the wrong way, especially by beginners and unexperienced...
Article
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Tumor recognition by T cells is essential for antitumor immunity. A comprehensive characterization of T cell diversity may be key to understanding the success of immunomodulatory drugs and failure of PD-1 blockade in tumors such as multiple myeloma (MM). Here, we use single-cell RNA and T cell receptor sequencing to characterize bone marrow T cells...
Conference Paper
Research has shown how data sets convey social bias in AI systems, especially those based on machine learning. A biased data set is not representative of reality and might contribute to perpetuate societal biases within the model. To tackle this problem, it is important to understand how to avoid biases, errors, and unethical practices while creat...
Conference Paper
Full-text available
EXplainable Artificial Intelligence (XAI) is a vibrant research topic in the artificial intelligence community. It is raising growing interest across methods and domains, especially those involving high stake decision-making, such as the biomedical sector. Much has been written about the subject, yet XAI still lacks shared terminology and a framewo...
Preprint
This empirical study proposes a novel methodology to measure users' perceived trust in an Explainable Artificial Intelligence (XAI) model. To do so, users' mental models are elicited using Fuzzy Cognitive Maps (FCMs). First, we exploit an interpretable Machine Learning (ML) model to classify suspected COVID-19 patients into positive or negative cas...
Article
Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This research was partly funded by the Italian Ministry of Health Introduction With the worldwide diffusion of cardiac magnetic resonance (CMR), demand on image quality has grown. CMR late gadolinium enhancement (LGE) imaging provides...
Conference Paper
Full-text available
Nowadays, physicians have at their hands a huge amount of data produced by a large set of diagnostic and instrumental tests integrated with data obtained by high-throughput technologies. If such data were opportunely linked and analysed, they might be used to strengthen predictions, so that to improve the prevention and the time-to-diagnosis, reduc...
Article
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Purpose Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming inc...
Article
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Introduction: Oxford Nanopore Technologies (ONT) is a third generation sequencing approach that allows the analysis of individual, full-length nucleic acids. ONT records the alterations of an ionic current flowing across a nano-scaled pore while a DNA or RNA strand is threading through the pore. Basecalling methods are then leveraged to translate t...
Preprint
Full-text available
The need for fully human-understandable models is increasingly being recognised as a central theme in AI research. The acceptance of AI models to assist in decision making in sensitive domains will grow when these models are interpretable, and this trend towards interpretable models will be amplified by upcoming regulations. One of the killer appli...
Article
Full-text available
The investigation of the emergent collective behaviour in flying birds is a challenging task, yet it has always fascinated scientists from different disciplines. In the attempt of studying and modelling line formation, we collected high-precision position data of 29 free-flying northern bald ibises (Geronticus eremita) using Global Navigation Satel...
Article
Computational Intelligence (CI) provides a set of powerful tools to effectively tackle complex computational tasks: global optimization methods (e.g., evolutionary computation, swarm intelligence), machine learning (e.g., neural networks), fuzzy reasoning, and so on. While CI research generally focuses on the improvement of algorithms (e.g., faster...
Article
Introduction Identifying the optimal Inversion Time (TI) is pivotal to null the myocardium and obtain high quality cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) imaging. Setting the optimal TI can be challenging in some diseases and for less experienced operators. We propose an Artificial Intelligence (AI) tool to automatically...
Article
Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventric...
Conference Paper
Full-text available
This conference will bring together top researchers, practitioners, academics, and students from around the globe to discuss the latest advances in the field of computational intelligence and other areas of artificial intelligence, as applied to real-world problems in biology, bioinformatics, computational biology, chemical informatics, bioengineer...
Article
Artificial intelligence is getting a foothold in medicine for disease screening and diagnosis. While typical machine learning methods require large labeled datasets for training and validation, their application is limited in clinical fields since ground truth information can hardly be obtained on a sizeable cohort of patients. Unsupervised neural...
Conference Paper
Abstract—In this paper we propose a fuzzy logic-based approach to analyze NHS public administrative data related to pre- and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial Intelligence systems, including our own, to take critical decisions having a significant impact on patients, su...
Article
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The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the bi...
Article
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Calcium homeostasis and signaling processes in Saccharomyces cerevisiae, as well as in any eukaryotic organism, depend on various transporters and channels located on both the plasma and intracellular membranes. The activity of these proteins is regulated by a number of feedback mechanisms that act through the calmodulin-calcineurin pathway. When e...
Article
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Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of computational systems biology can be limited by the lack of a standardized benchmarking approach. To overcome these limita...
Article
In the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum. Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide an interesting optim...
Article
Full-text available
Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanist...
Article
Full-text available
Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiom...
Preprint
Full-text available
Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of Computational Systems Biology can be limited by the lack of a standardized benchmarking approach. To overcome these limita...
Article
Full-text available
Bayesian Networks have been widely used in the last decades in many _elds, to describe statistical dependencies among random variables. In general, learning the structure of such models is a problem with considerable theoretical interest that poses many challenges. On the one hand, it is a well-known NP-complete problem, practically hardened by the...
Article
Funding Acknowledgements Type of funding sources: None. Introduction The extent of ischemic scar detected by Cardiac Magnetic Resonance (CMR) with late gadolinium enhancement (LGE) is linked with long-term prognosis, but scar quantification is time-consuming. Deep Learning (DL) approaches appear promising in CMR segmentation. Purpose: To train and...
Preprint
Full-text available
In the crowded environment of bio-inspired population-based meta-heuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum. Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide interesting optimiz...
Article
Full-text available
Background: Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized anal...
Article
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Background Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of reactions that sink nutrients into...
Article
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Self-assembling processes are ubiquitous phenomena that drive the organization and the hierarchical formation of complex molecular systems. The investigation of assembling dynamics, emerging from the interactions among biomolecules like amino-acids and polypeptides, is fundamental to determine how a mixture of simple objects can yield a complex str...
Article
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Combination therapies proved to be a valuable strategy in the fight against cancer, thanks to their increased efficacy in inducing tumor cell death and in reducing tumor growth, metastatic potential, and the risk of developing drug resistance. The identification of effective combinations of drug targets generally relies on costly and time consuming...
Article
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Ras oncoproteins play a crucial role in the onset, maintenance, and progression of the most common and deadly human cancers. Despite extensive research efforts, only a few mutant-specific Ras inhibitors have been reported. We show that cmp4-previously identified as a water-soluble Ras inhibitor-targets multiple steps in the activation and downstrea...
Chapter
Acute myeloid leukemia (AML) is a highly frequent hematological malignancy, characterized by clinical and biological diversity, along with high relapse and mortality rates. The inherent functional and genetic intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Patient-derived x...
Article
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Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, na...
Article
Several mathematical formalisms can be exploited to model complex systems, in order to capture different features of their dynamic behavior and leverage any available quantitative or qualitative data. Correspondingly, either quantitative models or qualitative models can be defined; bridging the gap between these two worlds would allow us to simulta...
Preprint
Full-text available
Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, na...
Chapter
Fuzzy inference systems (FIS) gained popularity and found application in several fields of science over the last years, because they are more transparent and interpretable than other common (black-box) machine learning approaches. However, transparency is not automatically achieved when FIS are estimated from data, thus researchers are actively inv...
Article
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Surfing in rough waters is not always as fun as wave riding the “big one”. Similarly, in optimization problems, fitness landscapes with a huge number of local optima make the search for the global optimum a hard and generally annoying game. Computational Intelligence optimization metaheuristics use a set of individuals that “surf” across the fitnes...
Chapter
Mathematical modeling and computational analyses are essential tools to understand and gain novel insights on the functioning of complex biochemical systems. In the specific case of metabolic reaction networks, which are regulated by many other intracellular processes, various challenging problems hinder the definition of compact and fully calibrat...
Chapter
In the latter years, detailed genome-wide metabolic models have been proposed, paving the way to thorough investigations of the connection between genotype and phenotype in human cells. Nevertheless, classic modeling and dynamic simulation approaches—based either on differential equations integration, Markov chains or hybrid methods—are still unfea...
Chapter
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the...
Article
Full-text available
Ordinary differential equations (ODEs) are a widespread formalism for the mathematical modeling of natural and engineering systems, whose analysis is generally performed by means of numerical integration methods. However, real-world models are often characterized by stiffness, a circumstance that can lead to prohibitive execution times. In such cas...
Chapter
Image texture extraction and analysis are fundamental steps in Computer Vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance since they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic...