Παραδοτέο Π.2.3: Μηχανισμοί επερώτησης και ανάκτησης πληροφορίας

Το παρόν Παραδοτέο Π.2.3 περιλαμβάνει τα αποτελέσματα της υποδράσης ΥΔ2.3. Στην ενότητα 1 παρουσιάζουμε το γενικότερο πλαίσιο του προβλήματος. Στην ενότητα 2 προτείνεται ένα framework για την απάντηση ερωτημάτων συντομότερης διαδρομής σε γράφους κοινωνικών δικτύων με τη χρήση σχεσιακού συστήματος βάσεων δεδομένων. Στην ενότητα 3 παρουσιάζεται ένα εργαλείο για την ιεραρχική χαρτογράφηση και οπτική εξερεύνηση διασυνδεδεμένων δεδομένων, ενώ στην ενότητα 4 ερευνάται η αποδοτική πλοήγηση και οπτικοποίηση πολύ μεγάλων RDF γράφων. Στην ενότητα 5 παρουσιάζεται μια πρώτη προσέγγιση στο πρόβλημα της διαφοροποίησης αποτελεσμάτων αναζήτησης σε RDF δεδομένα. Στις ενότητες 6 και 7 αναφερόμαστε σε αποτελέσματα σχετικά με την αναζήτηση σε RDF δεδομένα

A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data, underpinning various tasks including node classification and link prediction. However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes. This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes. In this survey, we embark on a comprehensive review of the literature on imbalanced learning on graphs. We begin by providing a definitive understanding of the concept and related terminologies, establishing a strong foundational understanding for readers. Following this, we propose two comprehensive taxonomies: (1) the problem taxonomy, which describes the forms of imbalance we consider, the associated tasks, and potential solutions; (2) the technique taxonomy, which details key strategies for addressing these imbalances, and aids readers in their method selection process. Finally, we suggest prospective future directions for both problems and techniques within the sphere of imbalanced learning on graphs, fostering further innovation in this critical area.Comment: The collection of awesome literature on imbalanced learning on graphs: https://github.com/Xtra-Computing/Awesome-Literature-ILoG

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

A MACHINE LEARNING MODEL FOR PREDICTING ESSENTIAL GENES FROM PLASMODIUM FALCIPARUM METABOLIC NETWORK

The accurate prediction of essential metabolic genes (i.e., genes necessary for cell survival) in eukaryotic organisms is still a difficult task in bioinformatics, especially in pathogenic species like Plasmodium falciparum, the malaria causing parasite. The difficulty and cost in time and resources of experimental methods has necessitated the use of computational methods for gene essentiality prediction. The majority of earlier research in this field concentrated on prokaryotes, omitting the complexity of weighted and directed metabolite transport in metabolic networks. To overcome this limitation, we developed a Network-based Machine Learning framework that explored various network properties in Plasmodium falciparum using the Genome-Scale Metabolic Model (iAM_Pf480) adopted from the BiGG database and essentiality data from the Ogee database. Our machine learning framework significantly increased the accuracy of gene essentiality predictions by taking into account the weighted and directed nature of the metabolic network and utilising network-based features, producing state-of-the-art results with an accuracy of 0.85 and AuROC of 0.7. This study expanded our knowledge of the complex nature of metabolic networks and their critical function in determining the essentiality of genes. Notably, our model identified important genes that were previously classified as non-essential in the Ogee database but predicted to be essential. Some of these genes have previously been linked to potential drug targets for the treatment of malaria, providing promising new research directions

ECM deposition is driven by caveolin-1-dependent regulation of exosomal biogenesis and cargo sorting.

The composition and physical properties of the extracellular matrix (ECM) critically influence tumor progression, but the molecular mechanisms underlying ECM layering are poorly understood. Tumor-stroma interaction critically depends on cell communication mediated by exosomes, small vesicles generated within multivesicular bodies (MVBs). We show that caveolin-1 (Cav1) centrally regulates exosome biogenesis and exosomal protein cargo sorting through the control of cholesterol content at the endosomal compartment/MVBs. Quantitative proteomics profiling revealed that Cav1 is required for exosomal sorting of ECM protein cargo subsets, including Tenascin-C (TnC), and for fibroblast-derived exosomes to efficiently deposit ECM and promote tumor invasion. Cav1-driven exosomal ECM deposition not only promotes local stromal remodeling but also the generation of distant ECM-enriched stromal niches in vivo. Cav1 acts as a cholesterol rheostat in MVBs, determining sorting of ECM components into specific exosome pools and thus ECM deposition. This supports a model by which Cav1 is a central regulatory hub for tumor-stroma interactions through a novel exosome-dependent ECM deposition mechanism.This study was supported by the Ministerio de Ciencia, Innovación y Universidades (CSD2009-0016, SAF2014-51876-R, SAF2017-83130-R, BFU2016-81912-REDC, and IGP-SO-MINSEV1512-07-2016); the Fundació La Marató de TV3 (385/C/2019); the Worldwide Cancer Research Foundation (AICR 15-0404); and Fondo Europeo de Desarrollo Regional “Una manera de hacer Europa” (to M.Á. del Pozo). M.Á. del Pozo’s group received funding from the European Union Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement no. 641639. M.Á. del Pozo is a member of the Tec4Bio consortium (ref. S2018/NMT4443; Actividades de I+D entre Grupos de Investigación en Tecnologías, Comunidad Autónoma de Madrid/FEDER, Spain). J. Balsinde was supported by Ministerio de Ciencia, Innovación y Universidades grants SAF2013-48201-R and SAF2016-80883-R, and G. Orend was supported by Institut National de la Santé et de la Recherche Médicale, the University of Strasbourg, the Ligue Contre le Cancer, and the Institut National du Cancer (ref. TENPLAMET). L. Albacete-Albacete was supported by a Ministerio de Ciencia, Innovación y Universidades predoctoral fellowship associated with the Severo Ochoa Excellence program (ref. SVP-2013-06789). I. Navarro-Lérida was supported by a postdoctoral fellowship from the Asociación Española Contra el Cáncer (ref. INVES191NAVA). The Centro Nacional de Investigaciones Cardiovasculares Carlos III is supported by the Ministerio de Ciencia e Innovación and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S

New Data Protection Abstractions for Emerging Mobile and Big Data Workloads

Two recent shifts in computing are challenging the effectiveness of traditional approaches to data protection. Emerging machine learning workloads have complex access patterns and unique leakage characteristics that are not well supported by existing protection approaches. Second, mobile operating systems do not provide sufficient support for fine grained data protection tools forcing users to rely on individual applications to correctly manage and protect data. My thesis is that these emerging workloads have unique characteristics that we can leverage to build new, more effective data protection abstractions. This dissertation presents two new data protection systems for machine learning work-loads and a new system for fine grained data management and protection on mobile devices. First is Sage, a differentially private machine learning platform addressing the two primary challenges of differential privacy: running out of budget and the privacy utility tradeoff. The second system, Pyramid, is the first selective data system. Pyramid leverages count featurization to reduce the amount of data exposed while training classification models by two orders of magnitude. The final system, Pebbles, provides users with logical data objects as a new fine grained data management and protection primitive allowing data management at a higher level of abstraction. Pebbles, leverages high level storage abstractions in mobile operating systems to discover user recognizable application level data objects in unmodified mobile applications