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Introduction: Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods: We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results: MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region. Funding: the research leading to these results was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727658 (project iASiS), European Research Council ASTRA 855923, and the European Genome‐phenome Archive (EGA). Claudia Giambartolomei has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant agreement No 754490–MINDED project. This research has been conducted using the UK Biobank Resource under Application Number 35916

Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis.

We propose a knowledge framework for garment recommendations, which is based on two pillars. The first pillar incorporates knowledge about aspects of fashion, such as materials, garments, colours, body types, facial features, social occasion etc., as well as their interrelations, with the purpose of providing personalised recommendations. The said knowledge is encoded in the form of an owl ontology, the origin of which is attributed to fashion experts. Moreover, in commercial fashion sites, customers purchase garments of various types. Because of that, interesting patterns in their purchase behaviour can be sought, and thus groups of garments that tend to be purchased together can be discovered. This forms the second pillar, that can be used to enhance the first pillar with community based garment recommendations. This paper is the description and integration of the aforementioned pillars in a knowledge framework.

This paper presents a knowledge discovery framework for the construction of Community Web Directories, a concept that we introduced in our recent work, applying personalization to Web directories. In this context, the Web directory is viewed as a thematic hierarchy and personalization is realized by constructing user community models on the basis of usage data. In contrast to most of the work on Web usage mining, the usage data that are analyzed here correspond to user navigation throughout the Web, rather than a particular Web site, exhibiting as a result a high degree of thematic diversity. For modeling the user communities, we introduce a novel methodology that combines the users' browsing behavior with thematic information from the Web directories. Following this methodology, we enhance the clustering and probabilistic approaches presented in previous work and also present a new algorithm that combines these two approaches. The resulting community models take the form of Community Web Directories. The proposed personalization methodology is evaluated both on a specialized artificial and a general-purpose Web directory, indicating its potential value to the Web user. The experiments also assess the effectiveness of the different machine learning techniques on the task.

e20546Background: Lung cancer is a heterogeneous disease, with characteristics that differ between patients and even among regions of the same tumor. One of the main challenges of precision medicin...

This paper reports on the Large Scale Hierarchical Classification workshop (http://kmi.open.ac.uk/events/ecir2010/workshops-tutorials), held in conjunction with the European Conference on Information Retrieval (ECIR) 2010. The workshop was associated with the PASCAL 2 Large-Scale Hierarchical Text Classification Challenge (http://lshtc.iit.demokritos.gr), which took place in 2009. We first provide information about the challenge, presenting the data used, the tasks and the evaluation measures and then we provide an overview of the approaches proposed by the participants of the workshop, together with a summary of the results of the challenge.

Summarization: A synergetic understanding of cancer evolution and the effect of combination drug therapies on the disease is the cornerstone for developing effective personalized treatments, which can radically improve patients' well-being and their quality of (work and social) life. By extension, improving the treatment of patients indirectly enhances the quality of life for families, friends, and careers. Moreover, personalizing effective therapeutic approaches reduces treatment duration, cutting down healthcare monetary costs, which can be redirected to other health and social services. Given that three out of four U.S. families will at some point experience a family member suffering from cancer (http://natamcancer.org/NAP_Native_American_Priorities.pdf), the potential impact of improved cancer treatment is of considerable socio-economic and organizational significance. Παρουσιάστηκε στο: IEEE Technology and Society Magazine

Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis.

Artificial intelligence (AI) has brought significant transformations in various domains of everyday life, revolutionizing human-machine interactions. Intelligent agents, the fundamental components of AI systems, have the ability to perceive, reason, and act in their environment to achieve specific goals. Ranging from simple rule-based systems to complex deep learning models, these agents can be trained using a variety of learning schemes falling under the broader umbrella of machine learning (ML). One influential sub-field of ML that has gained considerable attention is reinforcement learning (RL). RL focuses on training intelligent agents to make sequential decisions by interacting with an environment, drawing inspiration from trial-and-error learning observed in humans and animals. Another ML technique, known as imitation learning (IL), combines supervised learning and RL principles by learning from expert demonstrations. This dissertation explores the application of both RL and IL techniques in the context of traffic applications, addressing the significant challenges of (a) congestion management and (b) trajectory modeling. Traffic applications play a vital role in modern society, as they encompass a wide range of systems and technologies aimed at managing and optimizing the behavior and the navigation of vehicles including various modes of transportation such as ground vehicles, roadway systems, air vehicles, and sea vessels. Congestion occurs when the demand for shared resources exceeds supply, leading to reduced efficiency of the overall system. Trajectory modeling involves analyzing and predicting expert behaviors based on historical demonstrated data. These challenges are not limited to the traffic domain but may also be extended to other fields where similar optimization and decision-making problems arise. This PhD is separated in three parts where the following problems are studied: (a) urban traffic navigation, (b) air traffic management, and (c) aircraft trajectory prediction.In the first part, the aim is to create efficient multi-agent systems for controlling and navigating fleets of vehicles in unsignalized large-scale urban road networks with complex scenarios and noise. By employing multi-agent reinforcement learning (MARL) techniques, the study seeks to navigate vehicles safely, preventing collisions, and minimizing traveling time. The proposed research contributes to the advancement of intelligent traffic management systems by utilizing RL techniques to optimize traffic flow and reduce congestion in urban areas.The second part focuses on tackling congestion problems in the aviation domain, particularly focusing on the demand and capacity balance (DCB) problem in air traffic management (ATM). By employing MARL schemes and leveraging hierarchical frameworks, the study seeks to minimize flight delays, optimize airspace utilization, and reduce fuel consumption and operating costs. The cooperative behavior of the involved flights is enabled to achieve more efficient use of the airspace and enhance overall performance of the multi-agent system. The final part studies generative models and trajectory modeling techniques in the aviation domain. Trajectory prediction is of an utmost importance for congestion management, and IL techniques offer a promising approach by training agents to imitate expert behaviors. Multi-modal imitation learning can further enhance trajectory prediction by capturing various behavioral patterns exhibited during flight execution. By leveraging expert data and modeling distinct patterns, the proposed approach improves the accuracy and robustness of trajectory prediction systems, leading to enhanced air traffic management, optimized route planning, and safer and more efficient flights. Η τεχνητή νοημοσύνη (ΤΝ) έχει επιφέρει σημαντικές αλλαγές σε διάφορους τομείς της καθημερινής ζωής, φέρνοντας επανάσταση στις αλληλεπιδράσεις ανθρώπου-μηχανής. Οι ευφυείς πράκτορες, που αποτελούν τα θεμελιώδη συστατικά των συστημάτων ΤΝ, έχουν την ικανότητα να αντιλαμβάνονται, να συλλογίζονται και να ενεργούν στο περιβάλλον τους για να επιτύχουν συγκεκριμένους στόχους. Από απλά συστήματα βασισμένα σε κανόνες μέχρι πολύπλοκα μοντέλα βαθιάς μάθησης, οι ευφυείς μπορούν να εκπαιδευτούν χρησιμοποιώντας μια ποικιλία τεχνικών μάθησης που εμπίπτουν στο ευρύτερο πεδίο της μηχανικής μάθησης. Ενα σημαντικό υποπεδίο της μηχανικής μάθησης που έχει αντλήσει σημαντική προσοχή, είναι η ενισχυτική μάθηση. Η ενισχυτική μάθηση επικεντρώνεται στην εκπαίδευση ευφυών πρακτόρων για τη λήψη διαδοχικών αποφάσεων αλληλεπιδρώντας με ένα περιβάλλον, αντλώντας έμπνευση από τη μάθηση δοκιμής και σφάλματος που παρατηρείται σε ανθρώπους και ζώα. Μια άλλη τεχνική μηχανικής μάθησης, γνωστή και ως μάθηση μέσω μίμησης, συνδυάζει την εποπτευόμενη μάθηση και τις αρχές της ενισχυτικής μάθησης μαθαίνοντας από τις επιδείξεις ειδικών. Η παρούσα διατριβή διερευνά την εφαρμογή τόσο των τεχνικών ενισχυτικής μάθησης όσο και αυτών της μάθησης μέσω μίμησης στο πλαίσιο των εφαρμογών κυκλοφορίας, αντιμετωπίζοντας τις σημαντικές προκλήσεις της (α) διαχείρισης συμφόρησης και (β) μοντελοποίησης τροχιάς. Οι εφαρμογές της κυκλοφορίας διαδραματίζουν ζωτικό ρόλο στη σύγχρονη κοινωνία, καθώς περιλαμβάνουν ένα ευρύ φάσμα συστημάτων και τεχνολογιών που αποσκοπούν στη διαχείριση και βελτιστοποίηση xv της συμπεριφοράς και της πλοήγησης των οχημάτων, συμπεριλαμβανομένων των διαφόρων τύπων μεταφοράς, όπως τα οχήματα εδάφους, τα οδικά συστήματα, τα εναέρια οχήματα και τα θαλάσσια σκάφη. Η συμφόρηση συμβαίνει όταν η ζήτηση για διαμοιραζόμενους πόρους υπερβαίνει την προσφορά, οδηγώντας σε μειωμένη αποτελεσματικότητα του συνολικού συστήματος. Η μοντελοποίηση της τροχιάς περιλαμβάνει την ανάλυση και την πρόβλεψη συμπεριφορών από ειδικούς με βάση ιστορικά δεδομένα παραδειγμάτων. Αυτές οι προκλήσεις δεν περιορίζονται στον τομέα της κυκλοφορίας, αλλά μπορούν επίσης να επεκταθούν και σε άλλους τομείς όπου προκύπτουν παρόμοια προβλήματα βελτιστοποίησης και λήψης αποφάσεων. Το παρόν διδακτορικό χωρίζεται σε τρία μέρη όπου μελετώνται τα ακόλουθα προβλήματα: (α) πλοήγηση αστικής κυκλοφορίας, (β) διαχείριση εναέριας κυκλοφορίας, και (γ) πρόβλεψη τροχιάς αεροσκαφών. Στο πρώτο μέρος, ο στόχος είναι να δημιουργηθούν αποτελεσματικά πολυπρακτορικά συστήματα για τον έλεγχο και την πλοήγηση στόλων οχημάτων σε μη σηματοδοτούμενα μεγάλης κλίμακας αστικά οδικά δίκτυα με σύνθετα σενάρια και θόρυβο. Με τη χρήση τεχνικών πολυπρακτορικής ενισχυτικής μάθησης, η μελέτη επιδιώκει να πλογήσει με ασφάλεια τα οχήματα, αποτρέποντας τις συγκρούσεις και ελαχιστοποιώντας τον χρόνο ταξιδιού. Η προτεινόμενη έρευνα συμβάλλει στην πρόοδο των ευφυών συστημάτων διαχείρισης της κυκλοφορίας με τη χρήση τεχνικών ενισχυτικής μάθησης για τη βελτιστοποίηση της ροής της κυκλοφορίας και τη μείωση της συμφόρησης στις αστικές περιοχές. Το δεύτερο μέρος επικεντρώνεται στην αντιμετώπιση των προβλημάτων συμφόρησης στον τομέα των αερομεταφορών, με ιδιαίτερη έμφαση στο πρόβλημα ισορροπίας μεταξύ ζήτησης και χωρητικότητας στην διαχείριση του εναέριας κυκλοφορίας. Χρησιμοποιώντας πολυπρακτορικά συστήματα ενισχυτικής μάθησης και αξιοποιώντας ιεραρχικές δομές, η μελέτη επιδιώκει να ελαχιστοποιήσει τις καθυστερήσεις των πτήσεων, να βελτιστοποιήσει τη χρήση του εναέριου χώρου και να μειώσει την κατανάλωση καυσίμων και το λειτουργικό κόστος. Η συνεργατική συμπεριφορά των εμπλεκόμενων πτήσεων επιτρέπει την αποτελεσματικότερη χρήση του εναέριου χώρου και την ενίσχυση της συνολικής διαχείρισης του πολυπρακτορικού συστήματος. Το τελευταίο μέρος μελετά παραγωγικά μοντέλα μάθησης και τεχνικές μοντελοποίησης τροχιών στον εναέριο τομέα. Η πρόβλεψη τροχιάς είναι υψίστης σημασίας για τη διαχείριση της συμφόρησης και οι τεχνικές μάθησης μέσω μίμησης προσφέρουν μια πολλά υποσχόμενη προσέγγιση εκπαιδεύοντας πράκτορες με σκοπό να μιxvi μηθούν τις συμπεριφορές των ειδικών. Η πολυτροπική μάθηση μέσω μίμησης μπορεί να ενισχύσει περαιτέρω την πρόβλεψη της τροχιάς, αναλύοντας διάφορα πρότυπα συμπεριφοράς που εμφανίζονται κατά την εκτέλεση των πτήσεων. Αξιοποιώντας τα δεδομένα των ειδικών και μοντελοποιώντας διαφορετικά μοτίβα, η προτεινόμενη προσέγγιση βελτιώνει την ακρίβεια και την ευρωστία των συστημάτων πρόβλεψης τροχιάς, οδηγώντας σε βελτιωμένη διαχείριση της εναέριας κυκλοφορίας, βελτιστοποιημένο σχεδιασμό διαδρομών και ασφαλέστερες και αποδοτικότερες πτήσεις. 214