4,766 research outputs found

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    Information actors beyond modernity and coloniality in times of climate change:A comparative design ethnography on the making of monitors for sustainable futures in Curaçao and Amsterdam, between 2019-2022

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    In his dissertation, Mr. Goilo developed a cutting-edge theoretical framework for an Anthropology of Information. This study compares information in the context of modernity in Amsterdam and coloniality in Curaçao through the making process of monitors and develops five ways to understand how information can act towards sustainable futures. The research also discusses how the two contexts, that is modernity and coloniality, have been in informational symbiosis for centuries which is producing negative informational side effects within the age of the Anthropocene. By exploring the modernity-coloniality symbiosis of information, the author explains how scholars, policymakers, and data-analysts can act through historical and structural roots of contemporary global inequities related to the production and distribution of information. Ultimately, the five theses propose conditions towards the collective production of knowledge towards a more sustainable planet

    vONTSS: vMF based semi-supervised neural topic modeling with optimal transport

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    Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge. This work presents a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and optimal transport. When a few keywords per topic are provided, vONTSS in the semi-supervised setting generates potential topics and optimizes topic-keyword quality and topic classification. Experiments show that vONTSS outperforms existing semi-supervised topic modeling methods in classification accuracy and diversity. vONTSS also supports unsupervised topic modeling. Quantitative and qualitative experiments show that vONTSS in the unsupervised setting outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered and coherent topics on benchmark datasets. It is also much faster than the state-of-the-art weakly supervised text classification method while achieving similar classification performance. We further prove the equivalence of optimal transport loss and cross-entropy loss at the global minimum.Comment: 24 pages, 12 figures, ACL findings 202

    Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study

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    Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits

    Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

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    Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database

    Rational development of stabilized cyclic disulfide redox probes and bioreductive prodrugs to target dithiol oxidoreductases

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    Countless biological processes allow cells to develop, survive, and proliferate. Among these, tightly balanced regulatory enzymatic pathways that can respond rapidly to external impacts maintain dynamic physiological homeostasis. More specifically, redox homeostasis broadly affects cellular metabolism and proliferation, with major contributions by thiol/disulfide oxidoreductase systems, in particular, the Thioredoxin Reductase Thioredoxin (TrxR/Trx) and the Glutathione Reductase-Glutathione-Glutaredoxin (GR/GSH/Grx) systems. These cascades drive vital cellular functions in many ways through signaling, regulating other proteins' activity by redox switches, and by stoichiometric reductant transfers in metabolism and antioxidant systems. Increasing evidence argues that there is a persistent alteration of the redox environment in certain pathological states, such as cancer, that heavily involve the Trx system: upregulation and/or overactivity of the Trx system may support or drive cancer progression, making both TrxR and Trx promising targets for anti-cancer drug development. Understanding the biochemical mechanisms and connections between certain redox cascades requires research tools that interact with them. The state-of-the-art genetic tools are mostly ratiometric reporters that measure reduced:oxidized ratios of selected redox pairs or the general thiol pool. However, the precise cellular roles of the central oxidoreductase systems, including TrxR and Trx, remain inaccessible due to the lack of probes to selectively measure turnover by either of these proteins. However, such probes would allow measuring their effective reductive activity apart from expression levels in native systems, including in cells, animals, or patient samples. They are also of high interest to identify chemical inhibitors for TrxR/Trx in cells and to validate their potential use as anti-cancer agents (to date, there is no selective cellular Trx inhibitor, and most known TrxR inhibitors were not comprehensively evaluated considering selectivity and potential off-targets). However, small molecule redox imaging tools are underdeveloped: their protein specificity, spectral properties, and applicability remain poorly precedented. This work aimed to address this opportunity gap and develop novel, small molecule diagnostic and therapeutic tools to selectively target the Trx system based on a modular trigger cargo design: artificial cyclic disulfide substrates (trigger) for oxidoreductases are tethered to molecular agents (cargo) such that the cargo’s activity is masked and is re-established only through reduction by a target protein. The rational design of these novel reduction sensors to target the cell's strongest disulfide-reducing enzymes was driven by the following principles: (i) cyclic disulfide triggers with stabilized ring systems were used to gain low reduction potentials that should resist reduction except by the strongest cellular reductases, such as Trx; and (ii) the cyclic topology also offers the potential for kinetic reversibility that should select for dithiol-type redox proteins over the cellular monothiol background. Creating imaging agents based on such two-component designs to selectively measure redox protein activity in native cells required to combine the correct trigger reducibility, probe activation kinetics, and imaging modalities and to consider the overall molecular architecture. The major prior art in this field has applied cyclic 5-membered disulfides (1,2 dithiolanes) as substrates for TrxR in a similar way to create such tools. However, this motif was described elsewhere as thermodynamically instable and was due to widely used for dynamic covalent cascade reactions. By comparing a novel 1,2 dithiolane-based probe to the state-of-the-art probes, including commercial TrxR sensors, by screening a conclusive assay panel of cellular TrxR modulations, I clarified that 1,2 dithiolanes are not selective substrates for TrxR in biological settings (Nat Commun 2022). Instead, aiming for more stable ring systems and thus more robust redox probes, during this work, I developed bicyclic 6 membered disulfides (piperidine fused 1,2 dithianes) with remarkably low reduction potentials. I showed that molecular probes using them as reduction sensors can be mostly processed by thioredoxins while being stable against reduction by GSH. The thermodynamically stabilized decalin like topology of the cis-annelated 1,2 dithianes requires particularly strong reductants to be cleaved. They also select for dithiol type redox proteins, like Trx, based on kinetic reversibility and offer fast cyclization due to the preorganization by annelation (JACS 2021). This work further expanded the system’s modularity with structural cores based on piperazine-fused 1,2 dithianes with the two amines allowing independent derivatization. Diagnostic tools using them as reduction sensors proved equally robust but with highly improved activation kinetics and were thus cellularly activated. Cellular studies evolved that they are substrates for both Trxs and their protein cousins Grxs, so measuring the cellular dithiol protein pool rather than solely Trx activity (preprint 2023). Finally, a trigger based on a slightly adapted reduction sensor, a desymmetrized 1,2 thiaselenane, was designed for selective reduction by TrxR’s selenol/thiol active site, then combined with a precipitating large Stokes’ shift fluorophore and a solubilizing group, to evolve the first selective probe RX1 to measure cellular TrxR activity, which even allowed high throughput inhibitor screening (Chem 2022). The central principle of this work was further advanced to therapeutic prodrugs based on the duocarmycin cargo (CBI) with tunable potency (JACS Au 2022) that can be used to create off-to-on therapeutic prodrugs. Such CBI prodrugs employing stabilized 1,2 dichalcogenide triggers proved to be cytotoxins that depend on Trx system activity in cells. They could further be exploited for cell-line dependent reductase activity profiling by screening their redox activation indices, the reduction-dependent part of total prodrug activation, in 177 cell lines. Beyond that, these prodrugs were well-tolerated in animals and showed anti-cancer efficacy in vivo in two distinct mouse tumor models (preprint 2022). Taken together, I introduced unique monothiol-resistant reducible motifs to target the cellular Trx system with chemocompatible units for each for TrxR and Trx/Grx, where the cyclic nature of the dichalcogenides avoids activation by GSH. By using them with distinct molecular cargos, I developed novel selective fluorescent reporter probes; and introduced a new class of bioreductive therapeutic constructs based on a common modular design. These were either applied to selectively measure cellular reductase activity or to deliver cytotoxic anti cancer agents in vivo. Ongoing work aims to differentiate between the two major redox effector proteins Trx and Grx, requiring additional layers of selectivity that may be addressed by tuned molecular recognition. The flexible use of various molecular cargos allows harnessing the same cellular redox machinery by either probes or prodrugs. This allows predictive conclusions from diagnostics to be directly translated into therapy and offers great potential for future adaptation to other enzyme classes and therapeutic venues.Die zelluläre Redox-Homöostase hängt von Thiol/Disulfid-Oxidoreduktasen ab, die den Stoffwechsel, die Proliferation und die antioxidative Antwort von Zellen beeinflussen. Die wichtigsten Netzwerke sind die Thioredoxin Reduktase-Thioredoxin (TrxR/Trx) und Glutathion Reduktase-Glutathion-Glutaredoxin (GR/GSH/Grx) Systeme, die über Redox-Schalter in Substratproteinen lebenswichtige zelluläre Funktionen steuern und so an der Redox-Regulation und -Signalübertragung beteiligt sind. Persistente Veränderungen des Redoxmilieus in pathologischen Zuständen, wie z. B. bei Krebs, sind in hohem Maße mit dem Trx-System verbunden. Eine Hochregulierung und/oder Überaktivität des Trx-Systems, die bei vielen Krebsarten auftreten, unterstützt zudem das Fortschreiten des Krebswachstums, was TrxR/Trx zu vielversprechenden Zielproteinen für die Entwicklung neuer Krebsmedikamente macht. Um die biochemischen Prozesse dahinter zu erforschen, sind spezielle Techniken zur Visualisierung und Messung enzymatischer Aktivität nötig. Die hierzu geeigneten, meist genetischen Sensoren messen ratiometrisch das Verhältnis reduzierter/oxidierter Spezies in zellulärem Umfeld oder spezifisch ausgewählte Redoxpaare. Die weitere Erforschung der exakten Funktion von TrxR/Trx und deren Substrate ist jedoch durch mangelnde Nachweismethoden limitiert. Diese sind außerdem zur Validierung chemischer Hemmstoffe für TrxR/Trx in Zellen und deren potenziellen Verwendung als Krebsmittel von großem Interesse. Bislang gibt es keinen selektiven zellulären Trx-Inhibitor und potenzielle Off-Target-Effekte der bekannten TrxR-Inhibitoren wurden nicht abschließend bewertet. Ziel dieser Arbeit ist die Entwicklung niedermolekularer, diagnostischer und therapeutischer Werkzeuge, die selektiv auf das Trx-System abzielen und auf einem modularen Trigger-Cargo Design basieren. Hierzu werden zyklische Disulfid-Substrate (Trigger) für Oxidoreduktasen so mit molekularen Wirkstoffen (Cargo) verknüpft, dass dabei die Wirkstoffaktivität maskiert, und erst nach Reduktion durch ein Zielprotein wiederhergestellt wird. Diese neuartigen, synthetischen Reduktionssensoren basieren auf den folgenden Grundprinzipien: (i) Zyklische Disulfide sind thermodynamisch stabilisiert und können nur durch die stärksten Reduktasen gespalten werden; und (ii) die zyklische Topologie ermöglicht die kinetische Reversibilität der zwei Thiol-Disulfid-Austauschreaktionen, die eine erste Reaktion mit Monothiolen, wie z. B. GSH, sofort umkehrt und so eine vollständige Reduktion verhindert. Die meisten früheren Arbeiten auf diesem Gebiet verwendeten ein zyklisches, fünfgliedriges Disulfid (1,2 Dithiolan) als Substrat für TrxR. Das gleiche Strukturmotiv wurde jedoch an anderer Stelle als thermodynamisch instabil beschrieben und aufgrund dieser Eigenschaft explizit für dynamische Kaskadenreaktionen verwendet. Deshalb vergleicht diese Arbeit zu Beginn einen neuen 1,2 Dithiolan basierten fluorogenen Indikator mit bestehenden, z. T. kommerziellen, Redox Sonden für TrxR in einer Reihe von Zellkultur-Experimenten unter Modulation der zellulären TrxR Aktivität und stellt so einen Widerspruch in der Literatur klar: 1,2 Dithiolane eignen sich nicht als selektive Substrate für TrxR, da sie labil sowohl gegen die Reduktion durch andere Redoxproteine, als auch gegen den Monothiol Hintergrund in Zellen sind (Nat. Commun. 2022). Als alternatives Strukturmotiv wird in dieser Arbeit ein bizyklisches sechsgliedriges Disulfid (anneliertes 1,2 Dithian) etabliert. Durch sein niedriges Reduktionspotenzial, also seine hohe Resistenz gegen Reduktion, werden molekulare Sonden basierend auf diesem 1,2 Dithian als Reduktionssensor fast ausschließlich von Trx aktiviert, nicht aber von TrxR oder GSH (JACS 2021). Dieses Kernmotiv bestimmt dabei die Reduzierbarkeit, und damit die Enzymspezifität, durch seine zyklische Natur und die Annelierung, auch unter Verwendung unterschiedlicher Farb-/Wirkstoffe. Auf dieser Grundlage konnte die molekulare Struktur durch einen weiteren Modifikationspunkt für die flexible Verwendung weiterer funktioneller Einheiten ergänzt werden. Obwohl zelluläre Studien ergaben, dass diese neuartigen 1,2 Dithian Einheiten in Zellen sowohl Trx als auch das strukturell verwandte Grx adressieren, sind die daraus resultierenden diagnostischen Moleküle wertvoll, um den katalytischen Umsatz zellulärer Dithiol-Reduktasen, der sogenannten Trx Superfamilie, selektiv anzuzeigen (Preprint 2023). Begünstigt durch das modulare Moleküldesign stellt diese Arbeit zudem das erste Reportersystem RX1 zum selektiven Nachweis der TrxR-Aktivität in Zellen vor. Es basiert auf der Verwendung eines zyklischen, unsymmetrischen Selenenylsulfid-Sensors (1,2 Thiaselenan), der selektiv von dem einzigartigen Selenolat der TrxR angegriffen wird, und dadurch letztlich nur von TrxR reduziert werden kann. RX1 eignete sich zudem für eine Hochdurchsatz-Validierung bestehender TrxR Inhibitoren und unterstreicht dadurch den kommerziellen Nutzen derartiger Diagnostika (Chem 2022). Das zentrale Trigger-Cargo Konzept dieser Arbeit wurde für therapeutische Zwecke weiterentwickelt und nutzt dabei den einzigartigen Wirkmechanismus der Duocarmycin-Naturstoffklasse (CBI) (JACS Au 2022) zur Entwicklung reduktiv aktivierbarer Therapeutika. CBI Prodrugs basierend auf stabilisierten Redox-Schaltern (1,2 Dithiane für Trx; 1,2 Thiaselenan für TrxR) reagierten signifikant auf TrxR-Modulation in Zellen. Sie wurden darüber hinaus durch das Referenzieren ihrer Aktivität gegenüber nicht-reduzierbaren Kontrollmoleküle für die Erstellung zelllinienabhängiger Profile der Reduktaseaktivität in 177 Zelllinien genutzt. Schließlich waren diese neuen Krebsmittel im Tiermodell gut verträglich und zeigten in zwei verschiedenen Mausmodellen eine krebshemmende Wirkung (Preprint 2022b). Zusammenfassend präsentiert diese Dissertation monothiol-resistente reduzierbare Trigger-Einheiten für das zelluläre Trx-System zur Entwicklung neuartiger, selektiver Reporter-Sonden, sowie eine neue Klasse reduktiv aktivierbarer Krebsmittel auf Basis eines adaptierbaren Trigger-Cargo Designs. Diese fanden entweder zur selektiven Messung zellulärer Proteinaktivität oder zum Einsatz als Antikrebsmittel Verwendung. Es wurden chemokompatible Motive sowohl für TrxR als auch für Trx/Grx identifiziert, wobei deren zyklische Natur eine Aktivierung durch GSH verhindert. Eine weitere Differenzierung zwischen den beiden Redox-Proteinen Trx und Grx und anderen Proteinen der Trx-Superfamilie erfordert eine zusätzliche Ebene der Selektierung, z. B. durch molekulare Erkennung, und ist Gegenstand laufender Arbeiten. Die flexible Verwendung verschiedener molekularer Wirkstoffe ermöglicht dabei die „Pipeline-Entwicklung“ von Diagnostika und Therapeutika, die von der zellulären Redox-Maschinerie analog umgesetzt werden, und dadurch Schlussfolgerungen aus der Diagnostik direkt auf eine Therapie übertragbar machen. Dies birgt großes Potenzial für künftige Entwicklungen bei einer potenziellen Übertragung des modularen Konzepts auf andere Enzymklassen und therapeutische Einsatzgebiete

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    WL meet VC

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    Recently, many works studied the expressive power of graph neural networks (GNNs) by linking it to the 11-dimensional Weisfeiler--Leman algorithm (1-WL1\text{-}\mathsf{WL}). Here, the 1-WL1\text{-}\mathsf{WL} is a well-studied heuristic for the graph isomorphism problem, which iteratively colors or partitions a graph's vertex set. While this connection has led to significant advances in understanding and enhancing GNNs' expressive power, it does not provide insights into their generalization performance, i.e., their ability to make meaningful predictions beyond the training set. In this paper, we study GNNs' generalization ability through the lens of Vapnik--Chervonenkis (VC) dimension theory in two settings, focusing on graph-level predictions. First, when no upper bound on the graphs' order is known, we show that the bitlength of GNNs' weights tightly bounds their VC dimension. Further, we derive an upper bound for GNNs' VC dimension using the number of colors produced by the 1-WL1\text{-}\mathsf{WL}. Secondly, when an upper bound on the graphs' order is known, we show a tight connection between the number of graphs distinguishable by the 1-WL1\text{-}\mathsf{WL} and GNNs' VC dimension. Our empirical study confirms the validity of our theoretical findings.Comment: arXiv admin note: text overlap with arXiv:2206.1116

    Explainable temporal data mining techniques to support the prediction task in Medicine

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    In the last decades, the increasing amount of data available in all fields raises the necessity to discover new knowledge and explain the hidden information found. On one hand, the rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, results to users. In the biomedical informatics and computer science communities, there is considerable discussion about the `` un-explainable" nature of artificial intelligence, where often algorithms and systems leave users, and even developers, in the dark with respect to how results were obtained. Especially in the biomedical context, the necessity to explain an artificial intelligence system result is legitimate of the importance of patient safety. On the other hand, current database systems enable us to store huge quantities of data. Their analysis through data mining techniques provides the possibility to extract relevant knowledge and useful hidden information. Relationships and patterns within these data could provide new medical knowledge. The analysis of such healthcare/medical data collections could greatly help to observe the health conditions of the population and extract useful information that can be exploited in the assessment of healthcare/medical processes. Particularly, the prediction of medical events is essential for preventing disease, understanding disease mechanisms, and increasing patient quality of care. In this context, an important aspect is to verify whether the database content supports the capability of predicting future events. In this thesis, we start addressing the problem of explainability, discussing some of the most significant challenges need to be addressed with scientific and engineering rigor in a variety of biomedical domains. We analyze the ``temporal component" of explainability, focusing on detailing different perspectives such as: the use of temporal data, the temporal task, the temporal reasoning, and the dynamics of explainability in respect to the user perspective and to knowledge. Starting from this panorama, we focus our attention on two different temporal data mining techniques. The first one, based on trend abstractions, starting from the concept of Trend-Event Pattern and moving through the concept of prediction, we propose a new kind of predictive temporal patterns, namely Predictive Trend-Event Patterns (PTE-Ps). The framework aims to combine complex temporal features to extract a compact and non-redundant predictive set of patterns composed by such temporal features. The second one, based on functional dependencies, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework. We then discuss the concept of approximation, the data complexity of deriving an APFD, the introduction of two new error measures, and finally the quality of APFDs in terms of coverage and reliability. Exploiting these methodologies, we analyze intensive care unit data from the MIMIC dataset
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