Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning

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

Automated Gene Classification using Nonnegative Matrix Factorization on Biomedical Literature

Understanding functional gene relationships is a challenging problem for biological applications. High-throughput technologies such as DNA microarrays have inundated biologists with a wealth of information, however, processing that information remains problematic. To help with this problem, researchers have begun applying text mining techniques to the biological literature. This work extends previous work based on Latent Semantic Indexing (LSI) by examining Nonnegative Matrix Factorization (NMF). Whereas LSI incorporates the singular value decomposition (SVD) to approximate data in a dense, mixed-sign space, NMF produces a parts-based factorization that is directly interpretable. This space can, in theory, be used to augment existing ontologies and annotations by identifying themes within the literature. Of course, performing NMF does not come without a price—namely, the large number of parameters. This work attempts to analyze the effects of some of the NMF parameters on both convergence and labeling accuracy. Since there is a dearth of automated label evaluation techniques as well as “gold standard” hierarchies, a method to produce “correct” trees is proposed as well as a technique to label trees and to evaluate those labels

Algorithmic advances in learning from large dimensional matrices and scientific data

University of Minnesota Ph.D. dissertation.May 2018. Major: Computer Science. Advisor: Yousef Saad. 1 computer file (PDF); xi, 196 pages.This thesis is devoted to answering a range of questions in machine learning and data analysis related to large dimensional matrices and scientific data. Two key research objectives connect the different parts of the thesis: (a) development of fast, efficient, and scalable algorithms for machine learning which handle large matrices and high dimensional data; and (b) design of learning algorithms for scientific data applications. The work combines ideas from multiple, often non-traditional, fields leading to new algorithms, new theory, and new insights in different applications. The first of the three parts of this thesis explores numerical linear algebra tools to develop efficient algorithms for machine learning with reduced computation cost and improved scalability. Here, we first develop inexpensive algorithms combining various ideas from linear algebra and approximation theory for matrix spectrum related problems such as numerical rank estimation, matrix function trace estimation including log-determinants, Schatten norms, and other spectral sums. We also propose a new method which simultaneously estimates the dimension of the dominant subspace of covariance matrices and obtains an approximation to the subspace. Next, we consider matrix approximation problems such as low rank approximation, column subset selection, and graph sparsification. We present a new approach based on multilevel coarsening to compute these approximations for large sparse matrices and graphs. Lastly, on the linear algebra front, we devise a novel algorithm based on rank shrinkage for the dictionary learning problem, learning a small set of dictionary columns which best represent the given data. The second part of this thesis focuses on exploring novel non-traditional applications of information theory and codes, particularly in solving problems related to machine learning and high dimensional data analysis. Here, we first propose new matrix sketching methods using codes for obtaining low rank approximations of matrices and solving least squares regression problems. Next, we demonstrate that codewords from certain coding scheme perform exceptionally well for the group testing problem. Lastly, we present a novel machine learning application for coding theory, that of solving large scale multilabel classification problems. We propose a new algorithm for multilabel classification which is based on group testing and codes. The algorithm has a simple inexpensive prediction method, and the error correction capabilities of codes are exploited for the first time to correct prediction errors. The third part of the thesis focuses on devising robust and stable learning algorithms, which yield results that are interpretable from specific scientific application viewpoint. We present Union of Intersections (UoI), a flexible, modular, and scalable framework for statistical-machine learning problems. We then adapt this framework to develop new algorithms for matrix decomposition problems such as nonnegative matrix factorization (NMF) and CUR decomposition. We apply these new methods to data from Neuroscience applications in order to obtain insights into the functionality of the brain. Finally, we consider the application of material informatics, learning from materials data. Here, we deploy regression techniques on materials data to predict physical properties of materials

Identifying disease-associated genes based on artificial intelligence

Identifying disease-gene associations can help improve the understanding of disease mechanisms, which has a variety of applications, such as early diagnosis and drug development. Although experimental techniques, such as linkage analysis, genome-wide association studies (GWAS), have identified a large number of associations, identifying disease genes is still challenging since experimental methods are usually time-consuming and expensive. To solve these issues, computational methods are proposed to predict disease-gene associations. Based on the characteristics of existing computational algorithms in the literature, we can roughly divide them into three categories: network-based methods, machine learning-based methods, and other methods. No matter what models are used to predict disease genes, the proper integration of multi-level biological data is the key to improving prediction accuracy. This thesis addresses some limitations of the existing computational algorithms, and integrates multi-level data via artificial intelligence techniques. The thesis starts with a comprehensive review of computational methods, databases, and evaluation methods used in predicting disease-gene associations, followed by one network-based method and four machine learning-based methods. The first chapter introduces the background information, objectives of the studies and structure of the thesis. After that, a comprehensive review is provided in the second chapter to discuss the existing algorithms as well as the databases and evaluation methods used in existing studies. Having the objectives and future directions, the thesis then presents five computational methods for predicting disease-gene associations. The first method proposed in Chapter 3 considers the issue of non-disease gene selection. A shortest path-based strategy is used to select reliable non-disease genes from a disease gene network and a differential network. The selected genes are then used by a network-energy model to improve its performance. The second method proposed in Chapter 4 constructs sample-based networks for case samples and uses them to predict disease genes. This strategy improves the quality of protein-protein interaction (PPI) networks, which further improves the prediction accuracy. Chapter 5 presents a generic model which applies multimodal deep belief nets (DBN) to fuse different types of data. Network embeddings extracted from PPI networks and gene ontology (GO) data are fused with the multimodal DBN to obtain cross-modality representations. Chapter 6 presents another deep learning model which uses a convolutional neural network (CNN) to integrate gene similarities with other types of data. Finally, the fifth method proposed in Chapter 7 is a nonnegative matrix factorization (NMF)-based method. This method maps diseases and genes onto a lower-dimensional manifold, and the geodesic distance between diseases and genes are used to predict their associations. The method can predict disease genes even if the disease under consideration has no known associated genes. In summary, this thesis has proposed several artificial intelligence-based computational algorithms to address the typical issues existing in computational algorithms. Experimental results have shown that the proposed methods can improve the accuracy of disease-gene prediction

Efficient feature reduction and classification methods

Durch die steigende Anzahl verfügbarer Daten in unterschiedlichsten Anwendungsgebieten nimmt der Aufwand vieler Data-Mining Applikationen signifikant zu. Speziell hochdimensionierte Daten (Daten die über viele verschiedene Attribute beschrieben werden) können ein großes Problem für viele Data-Mining Anwendungen darstellen. Neben höheren Laufzeiten können dadurch sowohl für überwachte (supervised), als auch nicht überwachte (unsupervised) Klassifikationsalgorithmen weitere Komplikationen entstehen (z.B. ungenaue Klassifikationsgenauigkeit, schlechte Clustering-Eigenschaften, …). Dies führt zu einem Bedarf an effektiven und effizienten Methoden zur Dimensionsreduzierung. Feature Selection (die Auswahl eines Subsets von Originalattributen) und Dimensionality Reduction (Transformation von Originalattribute in (Linear)-Kombinationen der Originalattribute) sind zwei wichtige Methoden um die Dimension von Daten zu reduzieren. Obwohl sich in den letzten Jahren vielen Studien mit diesen Methoden beschäftigt haben, gibt es immer noch viele offene Fragestellungen in diesem Forschungsgebiet. Darüber hinaus ergeben sich in vielen Anwendungsbereichen durch die immer weiter steigende Anzahl an verfügbaren und verwendeten Attributen und Features laufend neue Probleme. Das Ziel dieser Dissertation ist es, verschiedene Fragenstellungen in diesem Bereich genau zu analysieren und Verbesserungsmöglichkeiten zu entwickeln. Grundsätzlich, werden folgende Ansprüche an Methoden zur Feature Selection und Dimensionality Reduction gestellt: Die Methoden sollten effizient (bezüglich ihres Rechenaufwandes) sein und die resultierenden Feature-Sets sollten die Originaldaten möglichst kompakt repräsentieren können. Darüber hinaus ist es in vielen Anwendungsgebieten wichtig, die Interpretierbarkeit der Originaldaten beizubehalten. Letztendlich sollte der Prozess der Dimensionsreduzierung keinen negativen Effekt auf die Klassifikationsgenauigkeit haben - sondern idealerweise, diese noch verbessern. Offene Problemstellungen in diesem Bereich betreffen unter anderem den Zusammenhang zwischen Methoden zur Dimensionsreduzierung und der resultierenden Klassifikationsgenauigkeit, wobei sowohl eine möglichst kompakte Repräsentation der Daten, als auch eine hohe Klassifikationsgenauigkeit erzielt werden sollen. Wie bereits erwähnt, ergibt sich durch die große Anzahl an Daten auch ein erhöhter Rechenaufwand, weshalb schnelle und effektive Methoden zur Dimensionsreduzierung entwickelt werden müssen, bzw. existierende Methoden verbessert werden müssen. Darüber hinaus sollte natürlich auch der Rechenaufwand der verwendeten Klassifikationsmethoden möglichst gering sein. Des Weiteren ist die Interpretierbarkeit von Feature Sets zwar möglich, wenn Feature Selection Methoden für die Dimensionsreduzierung verwendet werden, im Fall von Dimensionality Reduction sind die resultierenden Feature Sets jedoch meist Linearkombinationen der Originalfeatures. Daher ist es schwierig zu überprüfen, wie viel Information einzelne Originalfeatures beitragen. Im Rahmen dieser Dissertation konnten wichtige Beiträge zu den oben genannten Problemstellungen präsentiert werden: Es wurden neue, effiziente Initialisierungsvarianten für die Dimensionality Reduction Methode Nonnegative Matrix Factorization (NMF) entwickelt, welche im Vergleich zu randomisierter Initialisierung und im Vergleich zu State-of-the-Art Initialisierungsmethoden zu einer schnelleren Reduktion des Approximationsfehlers führen. Diese Initialisierungsvarianten können darüber hinaus mit neu entwickelten und sehr effektiven Klassifikationsalgorithmen basierend auf NMF kombiniert werden. Um die Laufzeit von NMF weiter zu steigern wurden unterschiedliche Varianten von NMF Algorithmen auf Multi-Prozessor Systemen vorgestellt, welche sowohl Task- als auch Datenparallelismus unterstützen und zu einer erheblichen Reduktion der Laufzeit für NMF führen. Außerdem wurde eine effektive Verbesserung der Matlab Implementierung des ALS Algorithmus vorgestellt. Darüber hinaus wurde eine Technik aus dem Bereich des Information Retrieval -- Latent Semantic Indexing -- erfolgreich als Klassifikationsalgorithmus für Email Daten angewendet. Schließlich wurde eine ausführliche empirische Studie über den Zusammenhang verschiedener Feature Reduction Methoden (Feature Selection und Dimensionality Reduction) und der resultierenden Klassifikationsgenauigkeit unterschiedlicher Lernalgorithmen präsentiert. Der starke Einfluss unterschiedlicher Methoden zur Dimensionsreduzierung auf die resultierende Klassifikationsgenauigkeit unterstreicht dass noch weitere Untersuchungen notwendig sind um das komplexe Zusammenspiel von Dimensionsreduzierung und Klassifikation genau analysieren zu können.The sheer volume of data today and its expected growth over the next years are some of the key challenges in data mining and knowledge discovery applications. Besides the huge number of data samples that are collected and processed, the high dimensional nature of data arising in many applications causes the need to develop effective and efficient techniques that are able to deal with this massive amount of data. In addition to the significant increase in the demand of computational resources, those large datasets might also influence the quality of several data mining applications (especially if the number of features is very high compared to the number of samples). As the dimensionality of data increases, many types of data analysis and classification problems become significantly harder. This can lead to problems for both supervised and unsupervised learning. Dimensionality reduction and feature (subset) selection methods are two types of techniques for reducing the attribute space. While in feature selection a subset of the original attributes is extracted, dimensionality reduction in general produces linear combinations of the original attribute set. In both approaches, the goal is to select a low dimensional subset of the attribute space that covers most of the information of the original data. During the last years, feature selection and dimensionality reduction techniques have become a real prerequisite for data mining applications. There are several open questions in this research field, and due to the often increasing number of candidate features for various application areas (e.\,g., email filtering or drug classification/molecular modeling) new questions arise. In this thesis, we focus on some open research questions in this context, such as the relationship between feature reduction techniques and the resulting classification accuracy and the relationship between the variability captured in the linear combinations of dimensionality reduction techniques (e.\,g., PCA, SVD) and the accuracy of machine learning algorithms operating on them. Another important goal is to better understand new techniques for dimensionality reduction, such as nonnegative matrix factorization (NMF), which can be applied for finding parts-based, linear representations of nonnegative data. This ``sum-of-parts'' representation is especially useful if the interpretability of the original data should be retained. Moreover, performance aspects of feature reduction algorithms are investigated. As data grow, implementations of feature selection and dimensionality reduction techniques for high-performance parallel and distributed computing environments become more and more important. In this thesis, we focus on two types of open research questions: methodological advances without any specific application context, and application-driven advances for a specific application context. Summarizing, new methodological contributions are the following: The utilization of nonnegative matrix factorization in the context of classification methods is investigated. In particular, it is of interest how the improved interpretability of NMF factors due to the non-negativity constraints (which is of central importance in various problem settings) can be exploited. Motivated by this problem context two new fast initialization techniques for NMF based on feature selection are introduced. It is shown how approximation accuracy can be increased and/or how computational effort can be reduced compared to standard randomized seeding of the NMF and to state-of-the-art initialization strategies suggested earlier. For example, for a given number of iterations and a required approximation error a speedup of 3.6 compared to standard initialization, and a speedup of 3.4 compared to state-of-the-art initialization strategies could be achieved. Beyond that, novel classification methods based on the NMF are proposed and investigated. We can show that they are not only competitive in terms of classification accuracy with state-of-the-art classifiers, but also provide important advantages in terms of computational effort (especially for low-rank approximations). Moreover, parallelization and distributed execution of NMF is investigated. Several algorithmic variants for efficiently computing NMF on multi-core systems are studied and compared to each other. In particular, several approaches for exploiting task and/or data-parallelism in NMF are studied. We show that for some scenarios new algorithmic variants clearly outperform existing implementations. Last, but not least, a computationally very efficient adaptation of the implementation of the ALS algorithm in Matlab 2009a is investigated. This variant reduces the runtime significantly (in some settings by a factor of 8) and also provides several possibilities to be executed concurrently. In addition to purely methodological questions, we also address questions arising in the adaptation of feature selection and classification methods to two specific application problems: email classification and in silico screening for drug discovery. Different research challenges arise in the contexts of these different application areas, such as the dynamic nature of data for email classification problems, or the imbalance in the number of available samples of different classes for drug discovery problems. Application-driven advances of this thesis comprise the adaptation and application of latent semantic indexing (LSI) to the task of email filtering. Experimental results show that LSI achieves significantly better classification results than the widespread de-facto standard method for this special application context. In the context of drug discovery problems, several groups of well discriminating descriptors could be identified by utilizing the ``sum-of-parts`` representation of NMF. The number of important descriptors could be further increased when applying sparseness constraints on the NMF factors

Clustering of scientific fields by integrating text mining and bibliometrics.

De toenemende verspreiding van wetenschappelijke en technologische publicaties via het internet, en de beschikbaarheid ervan in grootschalige bibliografische databanken, leiden tot enorme mogelijkheden om de wetenschap en technologie in kaart te brengen. Ook de voortdurende toename van beschikbare rekenkracht en de ontwikkeling van nieuwe algoritmen dragen hiertoe bij. Belangrijke uitdagingen blijven echter bestaan. Dit proefschrift bevestigt de hypothese dat de nauwkeurigheid van zowel het clusteren van wetenschappelijke kennisgebieden als het classificeren van publicaties nog verbeterd kunnen worden door het integreren van tekstontginning en bibliometrie. Zowel de tekstuele als de bibliometrische benadering hebben voor- en nadelen, en allebei bieden ze een andere kijk op een corpus van wetenschappelijke publicaties of patenten. Enerzijds is er een schat aan tekstinformatie aanwezig in dergelijke documenten, anderzijds vormen de onderlinge citaties grote netwerken die extra informatie leveren. We integreren beide gezichtspunten en tonen hoe bestaande tekstuele en bibliometrische methoden kunnen verbeterd worden. De dissertatie is opgebouwd uit drie delen: Ten eerste bespreken we het gebruik van tekstontginningstechnieken voor informatievergaring en voor het in kaart brengen van kennis vervat in teksten. We introduceren en demonstreren het raamwerk voor tekstontginning, evenals het gebruik van agglomeratieve hiërarchische clustering. Voorts onderzoeken we de relatie tussen enerzijds de performantie van het clusteren en anderzijds het gewenste aantal clusters en het aantal factoren bij latent semantische indexering. Daarnaast beschrijven we een samengestelde, semi-automatische strategie om het aantal clusters in een verzameling documenten te bepalen. Ten tweede behandelen we netwerken die bestaan uit citaties tussen wetenschappelijke documenten en netwerken die ontstaan uit onderlinge samenwerkingsverbanden tussen auteurs. Dergelijke netwerken kunnen geanalyseerd worden met technieken van de bibliometrie en de grafentheorie, met als doel het rangschikken van relevante entiteiten, het clusteren en het ontdekken van gemeenschappen. Ten derde tonen we de complementariteit aan van tekstontginning en bibliometrie en stellen we mogelijkheden voor om beide werelden op correcte wijze te integreren. De performantie van ongesuperviseerd clusteren en van classificeren verbetert significant door het samenvoegen van de tekstuele inhoud van wetenschappelijke publicaties en de structuur van citatienetwerken. Een methode gebaseerd op statistische meta-analyse behaalt de beste resultaten en overtreft methoden die enkel gebaseerd zijn op tekst of citaties. Onze geïntegreerde of hybride strategieën voor informatievergaring en clustering worden gedemonstreerd in twee domeinstudies. Het doel van de eerste studie is het ontrafelen en visualiseren van de conceptstructuur van de informatiewetenschappen en het toetsen van de toegevoegde waarde van de hybride methode. De tweede studie omvat de cognitieve structuur, bibliometrische eigenschappen en de dynamica van bio-informatica. We ontwikkelen een methode voor dynamisch en geïntegreerd clusteren van evoluerende bibliografische corpora. Deze methode vergelijkt en volgt clusters doorheen de tijd. Samengevat kunnen we stellen dat we voor de complementaire tekst- en netwerkwerelden een hybride clustermethode ontwerpen die tegelijkertijd rekening houdt met beide paradigma's. We tonen eveneens aan dat de geïntegreerde zienswijze een beter begrip oplevert van de structuur en de evolutie van wetenschappelijke kennisgebieden.SISTA;

Investigating Citation Linkage Between Research Articles

In recent years, there has been a dramatic increase in scientific publications across the globe. To help navigate this overabundance of information, methods have been devised to find papers with related content, but they are lacking in the ability to provide specific information that a researcher may need without having to read hundreds of linked papers. The search and browsing capabilities of online domain specific scientific repositories are limited to finding a paper citing other papers, but do not point to the specific text that is being cited. Providing this capability to the research community will be beneficial in terms of the time required to acquire the amount of background information they need to undertake their research. In this thesis, we present our effort to develop a citation linkage framework for finding those sentences in a cited article that are the focus of a citation in a citing paper. This undertaking has involved the construction of datasets and corpora that are required to build models for focused information extraction, text classification and information retrieval. As the first part of this thesis, two preprocessing steps that are deemed to assist with the citation linkage task are explored: method mention extraction and rhetorical categorization of scientific discourse. In the second part of this thesis, two methodologies for achieving the citation linkage goal are investigated. Firstly, regression techniques have been used to predict the degree of similarity between citation sentences and their equivalent target sentences with medium Pearson correlation score between predicted and expected values. The resulting learning models are then used to rank sentences in the cited paper based on their predicted scores. Secondly, search engine-like retrieval techniques have been used to rank sentences in the cited paper based on the words contained in the citation sentence. Our experiments show that it is possible to find the set of sentences that a citation refers to in a cited paper with reasonable performance. Possible applications of this work include: creation of better science paper repository navigation tools, development of scientific argumentation across research articles, and multi-document summarization of science articles