There exist a large number of rare genetic diseases in humans. Our knowledge of the specific gene variants whose presence in the genome of a person predisposes them towards developing a disease, called gene associations, is incomplete. Computational tools which can predict genes which may be associated with a rare disease have great utility in healthcare. However, a majority of existing prediction algorithms require a set of already known "seed genes'' to further discover novel associations for a disease. This drawback becomes more serious for rare genetic diseases, since a large proportion do not have any known gene associations. In this work, we develop an approach for disease-gene association prediction that overcomes the reliance on seed genes. Our approach uses the similarity of the observable biological characteristics of diseases (i.e., phenotypes) along with a global map of direct and indirect human protein interactions, to transfer associations from diseases whose gene associations have been discovered to diseases with no known gene associations. We formulate disease-gene association prediction over a multimodal network of diseases and genes, and develop an approach based on graph convolutional networks. We show how our model design considerations impact prediction performance. We demonstrate that our approach outperforms simpler graph machine learning and traditional machine learning approaches, as well as a competitive network propagation based approach for the task of predicting disease-gene associations. / Master of Science / There exist a large number of rare genetic diseases in humans. Our knowledge of the specific gene variants whose presence in the genome of a person predisposes them towards developing a disease, called gene associations, is incomplete. Computational tools which can predict genes which may be associated with a rare disease have great utility in healthcare. However, a majority of existing prediction algorithms require a set of already known "seed genes'' to further discover novel associations for a disease. This drawback becomes more serious for rare genetic diseases, since a large proportion do not have any known gene associations. In this work, we develop an approach for disease-gene association prediction that overcomes the reliance on seed genes. Our approach uses the similarity of the observable biological characteristics of diseases (i.e. disease phenotypes) along with a global map of direct and indirect human protein interactions, to transfer gene associations from diseases whose gene associations have been discovered, to diseases with no known associations. We implement an approach based on the field of graph machine learning, namely graph convolutional networks, to predict the genes associated with rare genetic diseases. We show how our predictor performs, compared to other approaches, and analyze some of the choices made in the design of the predictor, along with some properties of the outputs of our predictor.

A gene regulatory network (GRN) records the interactions among transcription factors and their target genes. GRNs are useful to study how transcription factors (TFs) control gene expression as cells transition between states during differentiation and development. Scientists usually construct GRNs by careful examination and study of the literature. This process is slow and painstaking and does not scale to large networks. In this thesis, we study the problem of inferring GRNs automatically from gene expression data. Recent data-driven approaches to infer GRNs increasingly rely on single-cell level RNA-sequencing (scRNA-seq) data. Most of these methods rely on unsupervised or association based strategies, which cannot leverage known regulatory interactions by design. To facilitate supervised learning, we propose a novel graph convolutional neural network (GCN) based autoencoder to infer new regulatory edges from a known GRN and scRNA-seq data. As the name suggests, a GCN-based autoencoder consists of an encoder that learns a low-dimensional embedding of the nodes (genes) in the input graph (the GRN) through a series of graph convolution operations and a decoder that aims to reconstruct the original graph as accurately as possible. We investigate several GCN-based architectures to determine the ideal encoder-decoder combination for GRN reconstruction. We systematically study the performance of these and other supervised learning methods on different mouse and human scRNA-seq datasets for two types of evaluation. We demonstrate that our GCN-based approach substantially outperforms traditional machine learning approaches. / Master of Science / In multi-cellular living organisms, stem cells differentiate into multiple cell types. Proteins called transcription factors (TFs) control the activity of genes to effect these transitions. It is possible to represent these interactions abstractly using a gene regulatory network (GRN). In a GRN, each node is a TF or a gene and each edge connects a TF to a gene or TF that it controls. New high-throughput technologies that can measure gene expression (activity) in individual cells provide rich data that can be used to construct GRNs. In this thesis, we take advantage of recent advances in the field of machine learning to develop a new computational method for computationally constructing GRNs. The distinguishing property of our technique is that it is supervised, i.e., it uses experimentally-known interactions to infer new regulatory connections. We investigate several variations of this approach to reconstruct a GRN as close to the original network as possible. We analyze and provide a rationale for the decisions made in designing, evaluating, and choosing the characteristics of our predictor. We show that our predictor has a reconstruction accuracy that is superior to other supervised-learning approaches.

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task using existing biomedical knowledge bases. Our approaches can be broadly labeled as link prediction or knowledge base completion in computer science literature. Specifically, first we investigate the predictive power of graph paths connecting entities in the publicly available biomedical knowledge base, SemMedDB (the entities and relations constitute a large knowledge graph as a whole). To that end, we build logistic regression models utilizing semantic graph pattern features extracted from the SemMedDB to predict treatment and causative relations in Unified Medical Language System (UMLS) Metathesaurus. Second, we study matrix and tensor factorization algorithms for predicting drug repositioning pairs in repoDB, a general purpose gold standard database of approved and failed drug–disease indications. The idea here is to predict repoDB pairs by approximating the given input matrix/tensor structure where the value of a cell represents the existence of a relation coming from SemMedDB and UMLS knowledge bases. The essential goal is to predict the test pairs that have a blank cell in the input matrix/tensor based on the shared biomedical context among existing non-blank cells. Our final approach involves graph convolutional neural networks where entities and relation types are embedded in a vector space involving neighborhood information. Basically, we minimize an objective function to guide our model to concept/relation embeddings such that distance scores for positive relation pairs are lower than those for the negative ones. Overall, our results demonstrate that recent link prediction methods applied to automatically curated, and hence imprecise, knowledge bases can nevertheless result in high accuracy drug candidate prediction with appropriate configuration of both the methods and datasets used.

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This thesis is divided into two distinct chapters. In the first chapter, we apply network representation learning to the field of materials science in order to predict aluminum grain boundaries' properties and locate the most influential atoms and subgraphs within each grain boundary. We create fixed-length representations of the aluminum grain boundaries that successfully capture grain boundary structure and allow us to accurately predict grain boundary energy. We do this through two distinct methods. The first method we use is a graph convolutional neural network, a semi-supervised deep learning algorithm, and the second method is graph2vec, an unsupervised representation learning algorithm. The second chapter presents our dynamic global value chain network, the combination of the dynamic global supply chain network and the dynamic global strategic alliance network. Our global value chain network provides a level of scope and accessibility not found in any other global value chain network, commercial or academic. Through applications of network theory, we discover business applications that would increase the robustness and resilience of the global value chain. We accomplish this through an analysis of the static, dynamic, and community structure of our global value chain network.

The prediction of polypharmacy side effects is crucial to reduce the mortality and morbidity of patients suffering from complex diseases. However, its experimental prediction is unfeasible due to the many possible drug combinations, leaving in silico tools as the most promising way of addressing this problem. This thesis improves the performance and robustness of a state-of-the-art graph convolutional network designed to predict polypharmacy side effects, by feeding it with complexity properties of the drug-protein network. The modifications also involve the creation of a direct pipeline to reproduce the results and test it with different datasets. / För att minska dödligheten och sjukligheten hos patienter som lider av komplexa sjukdomar är det avgörande att kunna förutsäga biverkningar från polyfarmaci. Att experimentellt förutsäga biverkningarna är dock ogenomförbart på grund av det stora antalet möjliga läkemedelskombinationer, vilket lämnar in silico-verktyg som det mest lovande sättet att lösa detta problem. Detta arbete förbättrar prestandan och robustheten av ett av det senaste grafiska faltningsnätverken som är utformat för att förutsäga biverkningar från polyfarmaci, genom att mata det med läkemedel-protein-nätverkets komplexitetsegenskaper. Ändringarna involverar också skapandet av en direkt pipeline för att återge resultaten och testa den med olika dataset.

Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, Deep-RGCNs, with dense and residual skip connections between layers. These skip connections are known to be very successful with popular deep CNN-architectures such as ResNet and DenseNet. In our experiments, we investigate and compare the performance of Deep-RGCN with different baselines on multi-relational graph benchmark datasets, AIFB and MUTAG, and show how the deep architecture boosts the performance in the task of node property classification. We also study the training performance of Deep-RGCNs (with N layers) and discuss the gradient vanishing and over-smoothing problems common to deeper GCN architectures.

In this thesis, an anomaly detection framework is assessed and fine-tuned to detect and explain anomalies in a power station, where EtherCAT, an Industrial Control System, is employed for monitoring. The chosen framework is based on a previously published Graph Neural Network (GNN) model, utilizing attention mechanisms to capture complex relationships between diverse measurements within the EtherCAT system. To address the challenges in graph learning and improve model performance and computational efficiency, the study introduces a novel similarity thresholding approach. This approach dynamically selects the number of neighbors for each node based on their similarity instead of adhering to a fixed 'k' value, thus making the learning process more adaptive and efficient. Further in the exploration, the study integrates Extreme Value Theory (EVT) into the framework to set the anomaly detection threshold and assess its effectiveness. The effect of temporal features on model performance is examined, and the role of seconds of the day as a temporal feature is notably highlighted. These various methodological innovations aim to refine the application of the attention based GNN framework to the EtherCAT system. The results obtained in this study illustrate that the similarity thresholding approach significantly improves the model's F1 score compared to the standard TopK approach. The inclusion of seconds of the day as a temporal feature led to modest improvements in model performance, and the application of EVT as a thresholding technique was explored, although it did not yield significant benefits in this context. Despite the limitations, including the utilization of a single-day dataset for training, the thesis provides valuable insights for the detection of anomalies in EtherCAT systems, contributing both to the literature and the practitioners in the field. It lays the groundwork for future research in this domain, highlighting key areas for further exploration such as larger datasets, alternative anomaly detection techniques, and the application of the framework in streaming data environments. / I denna avhandling utvärderas och finslipas ett ramverk för att detektera och förklara anomalier på ett kraftverk, där EtherCAT, ett industriellt styrsystem, används för övervakning. Det valda ramverket är baserat på en tidigare publicerad graf neurala nätverksmodell (GNN) som använder uppmärksamhetsmekanismer för att fånga komplexa samband mellan olika mätningar inom EtherCAT-systemet. För att hantera utmaningar inom grafiskt lärande och förbättra modellens prestanda och beräkningseffektivitet introducerar studien en ny metod för likhetsgränsdragning. Denna metod väljer dynamiskt antalet grannar för varje nod baserat på deras likhet istället för att hålla sig till ett fast 'k'-värde, vilket gör inlärningsprocessen mer anpassningsbar och effektiv. I en vidare undersökning integrerar studien extremvärdesteori (EVT) i ramverket för att sätta tröskeln för detektering av anomalier och utvärdera dess effektivitet. Effekten av tidsberoende egenskaper på modellens prestanda undersöks, och sekunder av dagen som en tidsberoende egenskap framhävs särskilt. Dessa olika metodologiska innovationer syftar till att förädla användningen av det uppmärksamhetsbaserade GNN-ramverket på EtherCAT-systemet. Resultaten som erhållits i denna studie illustrerar att likhetsgränsdragning väsentligt förbättrar modellens F1-poäng jämfört med den standardiserade TopK-metoden. Inkluderingen av sekunder av dagen som en tidsberoende egenskap ledde till blygsamma förbättringar i modellens prestanda, och användningen av EVT som en tröskelmetod undersöktes, även om den inte gav några betydande fördelar i detta sammanhang. Trots begränsningarna, inklusive användningen av ett dataset för endast en dag för träning, ger avhandlingen värdefulla insikter för detektering av anomalier i EtherCAT-system, och bidrar både till litteraturen och praktiker inom området. Den lägger grunden för framtida forskning inom detta område, och belyser nyckelområden för ytterligare utforskning såsom större dataset, alternativa tekniker för detektering av anomalier och tillämpningen av ramverket i strömmande data-miljöer.

Instruction Scheduling is a back-end compiler optimisation technique that can provide significant performance gains. It refers to ordering instructions in a particular order to reduce latency for processors with instruction-level parallelism. At the present typical compilers use heuristics to perform instruction scheduling and solve other related non-polynomial complete problems. This thesis aims to present a machine learning-based approach to challenge heuristic methods concerning performance. In this thesis, a novel reinforcement learning (RL) based model for the instruction scheduling problem is developed including modelling features of processors such as forwarding, resource utilisation and treatment of the action space. An efficient optimal scheduler is presented to be used for an optimal schedule length based reward function, however, this is not used in the final results as a heuristic based reward function was deemed to be sufficient and faster to compute. Furthermore, an RL agent that interacts with the model of the problem is presented using three different types of graph neural networks for the state processing: graph conventional networks, graph attention networks, and graph attention based on the work of Lee et al. A simple two-layer neural network is also used for generating embeddings for the resource utilisation stages. The proposed solution is validated against the modelled environment and favourable but not significant improvements were found compared to the most common heuristic method. Furthermore, it was found that having embeddings relating to resource utilisation was very important for the explained variance of the RL models. Additionally, a trained model was tested in an actual compiler, however, no informative results were found likely due to register allocation or other compiler stages that occur after instruction scheduling. Future work should include improving the scalability of the proposed solution. / Instruktionsschemaläggning är en optimeringsteknik för kompilatorer som kan ge betydande prestandavinster. Det handlar om att ordna instruktioner i en viss ordning för att minska latenstiden för processorer med parallellitet på instruktionsnivå. För närvarande använder vanliga kompilatorer heuristiker för att utföra schemaläggning av instruktioner och lösa andra relaterade ickepolynomiala kompletta problem. Denna avhandling syftar till att presentera en maskininlärningsbaserad metod för att utmana heuristiska metoder när det gäller prestanda. I denna avhandling utvecklas en ny förstärkningsinlärningsbaserad (RL) modell för schemaläggning av instruktioner, inklusive modellering av processorns egenskaper såsom vidarebefordran, resursutnyttjande och behandling av handlingsutrymmet. En effektiv optimal schemaläggare presenteras för att eventuellt användas för belöningsfunktionen, men denna används inte i de slutliga resultaten. Dessutom presenteras en RL-agent som interagerar med problemmodellen och använder tre olika typer av grafneurala nätverk för tillståndsprocessering: grafkonventionella nätverk, grafuppmärksamhetsnätverk och grafuppmärksamhet baserat på arbetet av Lee et al. Ett enkelt neuralt nätverk med två lager används också för att generera inbäddningar för resursanvändningsstegen. Den föreslagna lösningen valideras mot den modellerade miljön och gynnsamma men inte signifikanta förbättringar hittades jämfört med den vanligaste heuristiska metoden. Dessutom visade det sig att det var mycket viktigt för den förklarade variansen i RL-modellerna att ha inbäddningar relaterade till resursutnyttjande. Dessutom testades en tränad modell i en verklig kompilator, men inga informativa resultat hittades, sannolikt på grund av registerallokering eller andra kompilatorsteg som inträffar efter schemaläggning av instruktioner. Framtida arbete bör inkludera att förbättra skalbarheten hos den föreslagna lösningen.

Le discours haineux est un problème sérieux sur les média sociaux. Dans ce mémoire, nous étudions le problème de détection automatique du langage haineux sur réseaux sociaux. Nous traitons ce problème comme un problème de classification de textes. La classification de textes a fait un grand progrès ces dernières années grâce aux techniques d’apprentissage profond. En particulier, les modèles utilisant un mécanisme d’attention tel que BERT se sont révélés capables de capturer les informations contextuelles contenues dans une phrase ou un texte. Cependant, leur capacité à saisir l’information globale sur le vocabulaire d’une langue dans une application spécifique est plus limitée. Récemment, un nouveau type de réseau de neurones, appelé Graph Convolutional Network (GCN), émerge. Il intègre les informations des voisins en manipulant un graphique global pour prendre en compte les informations globales, et il a obtenu de bons résultats dans de nombreuses tâches, y compris la classification de textes. Par conséquent, notre motivation dans ce mémoire est de concevoir une méthode qui peut combiner à la fois les avantages du modèle BERT, qui excelle en capturant des informations locales, et le modèle GCN, qui fournit les informations globale du langage. Néanmoins, le GCN traditionnel est un modèle d'apprentissage transductif, qui effectue une opération convolutionnelle sur un graphe composé d'éléments à traiter dans les tâches (c'est-à-dire un graphe de documents) et ne peut pas être appliqué à un nouveau document qui ne fait pas partie du graphe pendant l'entraînement. Dans ce mémoire, nous proposons d'abord un nouveau modèle GCN de vocabulaire (VGCN), qui transforme la convolution au niveau du document du modèle GCN traditionnel en convolution au niveau du mot en utilisant les co-occurrences de mots. En ce faisant, nous transformons le mode d'apprentissage transductif en mode inductif, qui peut être appliqué à un nouveau document. Ensuite, nous proposons le modèle Interactive-VGCN-BERT qui combine notre modèle VGCN avec BERT. Dans ce modèle, les informations locales captées par BERT sont combinées avec les informations globales captées par VGCN. De plus, les informations locales et les informations globales interagissent à travers différentes couches de BERT, ce qui leur permet d'influencer mutuellement et de construire ensemble une représentation finale pour la classification. Via ces interactions, les informations de langue globales peuvent aider à distinguer des mots ambigus ou à comprendre des expressions peu claires, améliorant ainsi les performances des tâches de classification de textes. Pour évaluer l'efficacité de notre modèle Interactive-VGCN-BERT, nous menons des expériences sur plusieurs ensembles de données de différents types -- non seulement sur le langage haineux, mais aussi sur la détection de grammaticalité et les commentaires sur les films. Les résultats expérimentaux montrent que le modèle Interactive-VGCN-BERT surpasse tous les autres modèles tels que Vanilla-VGCN-BERT, BERT, Bi-LSTM, MLP, GCN et ainsi de suite. En particulier, nous observons que VGCN peut effectivement fournir des informations utiles pour aider à comprendre un texte haiteux implicit quand il est intégré avec BERT, ce qui vérifie notre intuition au début de cette étude. / Hate speech is a serious problem on social media. In this thesis, we investigate the problem of automatic detection of hate speech on social media. We cast it as a text classification problem. With the development of deep learning, text classification has made great progress in recent years. In particular, models using attention mechanism such as BERT have shown great capability of capturing the local contextual information within a sentence or document. Although local connections between words in the sentence can be captured, their ability of capturing certain application-dependent global information and long-range semantic dependency is limited. Recently, a new type of neural network, called the Graph Convolutional Network (GCN), has attracted much attention. It provides an effective mechanism to take into account the global information via the convolutional operation on a global graph and has achieved good results in many tasks including text classification. In this thesis, we propose a method that can combine both advantages of BERT model, which is excellent at exploiting the local information from a text, and the GCN model, which provides the application-dependent global language information. However, the traditional GCN is a transductive learning model, which performs a convolutional operation on a graph composed of task entities (i.e. documents graph) and cannot be applied directly to a new document. In this thesis, we first propose a novel Vocabulary GCN model (VGCN), which transforms the document-level convolution of the traditional GCN model to word-level convolution using a word graph created from word co-occurrences. In this way, we change the training method of GCN, from the transductive learning mode to the inductive learning mode, that can be applied to new documents. Secondly, we propose an Interactive-VGCN-BERT model that combines our VGCN model with BERT. In this model, local information including dependencies between words in a sentence, can be captured by BERT, while the global information reflecting the relations between words in a language (e.g. related words) can be captured by VGCN. In addition, local information and global information can interact through different layers of BERT, allowing them to influence mutually and to build together a final representation for classification. In so doing, the global language information can help distinguish ambiguous words or understand unclear expressions, thereby improving the performance of text classification tasks. To evaluate the effectiveness of our Interactive-VGCN-BERT model, we conduct experiments on several datasets of different types -- hate language detection, as well as movie review and grammaticality, and compare them with several state-of-the-art baseline models. Experimental results show that our Interactive-VGCN-BERT outperforms all other models such as Vanilla-VGCN-BERT, BERT, Bi-LSTM, MLP, GCN, and so on. In particular, we have found that VGCN can indeed help understand a text when it is integrated with BERT, confirming our intuition to combine the two mechanisms.