Machine learning for managing structured and semi-structured data

As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen

Active Learning for Reducing Labeling Effort in Text Classification Tasks

Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on AL in a text classification setting and next to none has involved the more recent, state-of-the-art Natural Language Processing (NLP) models. Here, we present an empirical study that compares different uncertainty-based algorithms with BERT$_{base}$ as the used classifier. We evaluate the algorithms on two NLP classification datasets: Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore heuristics that aim to solve presupposed problems of uncertainty-based AL; namely, that it is unscalable and that it is prone to selecting outliers. Furthermore, we explore the influence of the query-pool size on the performance of AL. Whereas it was found that the proposed heuristics for AL did not improve performance of AL; our results show that using uncertainty-based AL with BERT$_{base}$ outperforms random sampling of data. This difference in performance can decrease as the query-pool size gets larger.Comment: Accepted as a conference paper at the joint 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021). This camera-ready version submitted to BNAIC/BENELEARN, adds several improvements including a more thorough discussion of related work plus an extended discussion section. 28 pages including references and appendice

Improving Cross-Lingual Transfer Learning for Event Detection

The widespread adoption of applications powered by Artificial Intelligence (AI) backbones has unquestionably changed the way we interact with the world around us. Applications such as automated personal assistants, automatic question answering, and machine-based translation systems have become mainstays of modern culture thanks to the recent considerable advances in Natural Language Processing (NLP) research. Nonetheless, with over 7000 spoken languages in the world, there still remain a considerable number of marginalized communities that are unable to benefit from these technological advancements largely due to the language they speak. Cross-Lingual Learning (CLL) looks to address this issue by transferring the knowledge acquired from a popular, high-resource source language (e.g., English, Chinese, or Spanish) to a less favored, lower-resourced target language (e.g., Urdu or Swahili). This dissertation leverages the Event Detection (ED) sub-task of Information Extraction (IE) as a testbed and presents three novel approaches that improve cross-lingual transfer learning from distinct perspectives: (1) direct knowledge transfer, (2) hybrid knowledge transfer, and (3) few-shot learning

Vermeidung von Repräsentationsheterogenitäten in realweltlichen Wissensgraphen

Knowledge graphs are repositories providing factual knowledge about entities. They are a great source of knowledge to support modern AI applications for Web search, question answering, digital assistants, and online shopping. The advantages of machine learning techniques and the Web's growth have led to colossal knowledge graphs with billions of facts about hundreds of millions of entities collected from a large variety of sources. While integrating independent knowledge sources promises rich information, it inherently leads to heterogeneities in representation due to a large variety of different conceptualizations. Thus, real-world knowledge graphs are threatened in their overall utility. Due to their sheer size, they are hardly manually curatable anymore. Automatic and semi-automatic methods are needed to cope with these vast knowledge repositories. We first address the general topic of representation heterogeneity by surveying the problem throughout various data-intensive fields: databases, ontologies, and knowledge graphs. Different techniques for automatically resolving heterogeneity issues are presented and discussed, while several open problems are identified. Next, we focus on entity heterogeneity. We show that automatic matching techniques may run into quality problems when working in a multi-knowledge graph scenario due to incorrect transitive identity links. We present four techniques that can be used to improve the quality of arbitrary entity matching tools significantly. Concerning relation heterogeneity, we show that synonymous relations in knowledge graphs pose several difficulties in querying. Therefore, we resolve these heterogeneities with knowledge graph embeddings and by Horn rule mining. All methods detect synonymous relations in knowledge graphs with high quality. Furthermore, we present a novel technique for avoiding heterogeneity issues at query time using implicit knowledge storage. We show that large neural language models are a valuable source of knowledge that is queried similarly to knowledge graphs already solving several heterogeneity issues internally.Wissensgraphen sind eine wichtige Datenquelle von Entitätswissen. Sie unterstützen viele moderne KI-Anwendungen. Dazu gehören unter anderem Websuche, die automatische Beantwortung von Fragen, digitale Assistenten und Online-Shopping. Neue Errungenschaften im maschinellen Lernen und das außerordentliche Wachstum des Internets haben zu riesigen Wissensgraphen geführt. Diese umfassen häufig Milliarden von Fakten über Hunderte von Millionen von Entitäten; häufig aus vielen verschiedenen Quellen. Während die Integration unabhängiger Wissensquellen zu einer großen Informationsvielfalt führen kann, führt sie inhärent zu Heterogenitäten in der Wissensrepräsentation. Diese Heterogenität in den Daten gefährdet den praktischen Nutzen der Wissensgraphen. Durch ihre Größe lassen sich die Wissensgraphen allerdings nicht mehr manuell bereinigen. Dafür werden heutzutage häufig automatische und halbautomatische Methoden benötigt. In dieser Arbeit befassen wir uns mit dem Thema Repräsentationsheterogenität. Wir klassifizieren Heterogenität entlang verschiedener Dimensionen und erläutern Heterogenitätsprobleme in Datenbanken, Ontologien und Wissensgraphen. Weiterhin geben wir einen knappen Überblick über verschiedene Techniken zur automatischen Lösung von Heterogenitätsproblemen. Im nächsten Kapitel beschäftigen wir uns mit Entitätsheterogenität. Wir zeigen Probleme auf, die in einem Multi-Wissensgraphen-Szenario aufgrund von fehlerhaften transitiven Links entstehen. Um diese Probleme zu lösen stellen wir vier Techniken vor, mit denen sich die Qualität beliebiger Entity-Alignment-Tools deutlich verbessern lässt. Wir zeigen, dass Relationsheterogenität in Wissensgraphen zu Problemen bei der Anfragenbeantwortung führen kann. Daher entwickeln wir verschiedene Methoden um synonyme Relationen zu finden. Eine der Methoden arbeitet mit hochdimensionalen Wissensgrapheinbettungen, die andere mit einem Rule Mining Ansatz. Beide Methoden können synonyme Relationen in Wissensgraphen mit hoher Qualität erkennen. Darüber hinaus stellen wir eine neuartige Technik zur Vermeidung von Heterogenitätsproblemen vor, bei der wir eine implizite Wissensrepräsentation verwenden. Wir zeigen, dass große neuronale Sprachmodelle eine wertvolle Wissensquelle sind, die ähnlich wie Wissensgraphen angefragt werden können. Im Sprachmodell selbst werden bereits viele der Heterogenitätsprobleme aufgelöst, so dass eine Anfrage heterogener Wissensgraphen möglich wird

Content Selection for Effective Counter-Argument Generation

The information ecosystem of social media has resulted in an abundance of opinions on political topics and current events. In order to encourage better discussions, it is important to promote high-quality responses and relegate low-quality ones. We thus focus on automatically analyzing and generating counter-arguments in response to posts on social media with the goal of providing effective responses. This thesis is composed of three parts. In the first part, we conduct an analysis of arguments. Specifically, we first annotate discussions from Reddit for aspects of arguments and then analyze them for their persuasive impact. Then we present approaches to identify the argumentative structure of these discussions and predict the persuasiveness of an argument. We evaluate each component independently using automatic or manual evaluations and show significant improvement in each. In the second part, we leverage our discoveries from our analysis in the process of generating counter-arguments. We develop two approaches in the retrieve-and-edit framework, where we obtain content using methods created during our analysis of arguments, among others, and then modify the content using techniques from natural language generation. In the first approach, we develop an approach to retrieve counter-arguments by annotating a dataset for stance and building models for stance prediction. Then we use our approaches from our analysis of arguments to extract persuasive argumentative content before modifying non-content phrases for coherence. In contrast, in the second approach we create a dataset and models for modifying content -- making semantic edits to a claim to have a contrasting stance. We evaluate our approaches using intrinsic automatic evaluation of our predictive models and an overall human evaluation of our generated output. Finally, in the third part, we discuss the semantic challenges of argumentation that we need to solve in order to make progress in the understanding of arguments. To clarify, we develop new methods for identifying two types of semantic relations -- causality and veracity. For causality, we build a distant-labeled dataset of causal relations using lexical indicators and then we leverage features from those indicators to build predictive models. For veracity, we build new models to retrieve evidence given a claim and predict whether the claim is supported by that evidence. We also develop a new dataset for veracity to illuminate the areas that need progress. We evaluate these approaches using automated and manual techniques and obtain significant improvement over strong baselines. Finally, we apply these techniques to claims in the domain of household electricity consumption, mining claims using our methods for causal relations and then verifying their truthfulness

Advancing systems biology of yeast through machine learning and comparative genomics

Synthetic biology has played a pivotal role in accomplishing the production of high value commodities, pharmaceuticals, and bulk chemicals. Fueled by the breakthrough of synthetic biology and metabolic engineering, Saccharomyces cerevisiae and various other yeasts (such as Yarrowia lipolytica, Pichia pastoris) have been proven to be promising microbial cell factories and are frequently used in scientific studies. However, the cellular metabolism and physiological properties for most of the yeast species have not been characterized in detail. To address these knowledge gaps, this thesis aims to leverage the large amounts of data available for yeast species and use state-of-the-art machine learning techniques and comparative genomic analysis to gain a deeper insight into yeast traits and metabolism.In this thesis, machine learning was applied to various unresolved biological problems on yeasts, i.e., gene essentiality, enzyme turnover number (kcat), and protein production. In the first part of the work, machine learning approaches were employed to predict gene essentiality based on sequence features and evolutionary features. It was demonstrated that the essential gene prediction could be substantially improved by integrating evolution-based features. Secondly, a high-quality deep learning model DLKcat was developed to predict kcat\ua0values by combining a graph neural network for substrates and a convolutional neural network for proteins. By predicting kcat profiles for 343 yeast/fungi species, enzyme-constrained models were reconstructed and used to further elucidate the cellular metabolism on a large scale. Lastly, a random forest algorithm was adopted to investigate feature importance analysis on protein production, it was found that post-translational modifications (PTMs) have a relatively higher impact on protein production compared with amino acid composition. In comparative genomics, a comprehensive toolbox HGTphyloDetect was developed to facilitate the identification of horizontal gene transfer (HGT) events. Case studies on some yeast species demonstrated the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, through systematic evolution analysis (e.g., HGT, gene family expansion) and genome-scale metabolic model simulation, the underlying mechanisms for substrate utilization were further probed across large-scale yeast species

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Towards Robust Machine Learning for Health Applications

Methoden des maschinellen Lernens haben über die letzten Jahrzehnte beeindruckende technologische Fortschritte ermöglicht und haben das Potenzial, viele Aspekte unseres Lebens nachhaltig zu verändern. Besonders vielversprechend ist maschinelles Lernen im Gesundheitsbereich. Hier kann es unser Verständnis immer komplexerer Gesundheitsdaten vertiefen, Prozesse wie Diagnostik und Risikoeinschätzung beschleunigen sowie deren Objektivität erhöhen, und eine personalisiertere medizinische Versorgung ermöglichen. Zugleich steht maschinelles Lernen im Gesundheitsbereich vor besonderen Herausforderungen. Gesundheitsdaten sind häufig zeitabhängig und heterogen, über mehrere Institutionen verteilt und nur in begrenztem Umfang für spezifische Modellierungsanwendungen zugänglich. Infolgedessen erfordert das maschinelle Lernen für den Gesundheitsbereich grundsätzlich robuste Methoden, die für heterogene und im Umfang begrenzte Daten geeignet sind, sowie besonders auf die jeweilige Anwendung zugeschnittene Modelle. Diese Dissertation umfasst Beiträge zu beiden dieser Aspekte. Sie enthält neue Methoden zur unüberwachten Domänenadaptation, die speziell für hochdimensionale molekulare Gesundheitsdaten entwickelt wurden und eine genauere Vorhersage über heterogene Datensätze hinweg ermöglichen. Als konkretes Anwendungsbeispiel wurden diese Methoden auf das Problem der Altersvorhersage basierend auf DNA-Methylierungsdaten über Gewebe hinweg angewandt. Im Vergleich zu einem nicht-adaptiven Referenzmodell verbesserten sie hierbei die Vorhersage auf einem Gewebe, das nicht zum Trainieren der Modelle verwendet wurde. Zusätzlich enthält diese Dissertation robuste Modelle zur Analyse von Daten einer frühen klinischen Studie, die die Verwendung von breitneutralisierenden Antikörpern zur Behandlung von HIV untersuchte. Hier wurden Modelle und Methoden gewählt, die trotz des begrenzten Stichprobenumfangs Heterogenität zwischen Patientengruppen berücksichtigen konnten. Ein weiterer anwendungsspezifischer Beitrag war die Entwicklung robuster Modelle zur zeitabhängigen Vorhersage der Mortalität sowie einer Cytomegalievirus-Reaktivierung nach hämatopoetischer Stammzelltransplantation. Diese Modelle wurden in einer prospektiven, nicht-interventionellen klinischen Studie validiert und generierten in einem Pilot-Vergleich eine ähnliche genaue Vorhersage wie die Einschätzung erfahrener Kliniker. Zusätzlich unterstützte diese Dissertation die Entwicklung der XplOit-Plattform, einer Software-Plattform, die robustes maschinelles Lernen für den Gesundheitsbereich durch die semantische Integration heterogener Daten erleichtert.Machine learning has enabled striking technological advances over the last decades and has the potential to transform many aspects of our lives. Its application is especially promising in the health domain, where it can improve our understanding of increasingly complex health data, accelerate processes such as diagnosis or risk assessment while also making them more objective, and enable a more personalized approach to medicine. At the same time, machine learning for health faces particular challenges. Health data is often temporal and heterogeneous, distributed across many institutions, and accessible only in modest amounts for a specific machine learning application. Consequently, machine learning for health requires generally robust methods capable of handling heterogeneous and limited data and models that are well-tailored to the task at hand. This thesis contributes to both of these aspects. It includes new methods for unsupervised domain adaptation, which were designed for high-dimensional molecular health data and improved prediction across heterogeneous datasets. As a concrete application example, these methods were applied to the problem of age prediction from DNA methylation data across tissues, where they improved age prediction on a tissue not used for model training compared to a non-adaptive reference model. In addition, this thesis includes robust models for the analysis of data from an early clinical trial evaluating the use of broadly neutralizing antibodies for the treatment of HIV, which were suitable to account for heterogeneity between patient groups despite a limited sample size. Another application-specific contribution was the development of robust models for the time-dependent prediction of mortality and early cytomegalovirus reactivation after hematopoietic cell transplantation. These models were validated in a prospective non-interventional clinical trial and demonstrated similar performance as experienced physicians in a pilot comparison. Finally, this thesis supported the development of the XplOit platform, a software platform that facilitates robust machine learning for health by semantically integrating heterogeneous datasets

Network-driven strategies to integrate and exploit biomedical data

[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina