Combining DNA Methylation with Deep Learning Improves Sensitivity and Accuracy of Eukaryotic Genome Annotation

Thesis (Ph.D.) - Indiana University, School of Informatics, Computing, and Engineering, 2020The genome assembly process has significantly decreased in computational complexity since the advent of third-generation long-read technologies. However, genome annotations still require significant manual effort from scientists to produce trust-worthy annotations required for most bioinformatic analyses. Current methods for automatic eukaryotic annotation rely on sequence homology, structure, or repeat detection, and each method requires a separate tool, making the workflow for a final product a complex ensemble. Beyond the nucleotide sequence, one important component of genetic architecture is the presence of epigenetic marks, including DNA methylation. However, no automatic annotation tools currently use this valuable information. As methylation data becomes more widely available from nanopore sequencing technology, tools that take advantage of patterns in this data will be in demand. The goal of this dissertation was to improve the annotation process by developing and training a recurrent neural network (RNN) on trusted annotations to recognize multiple classes of elements from both the reference sequence and DNA methylation. We found that our proposed tool, RNNotate, detected fewer coding elements than GlimmerHMM and Augustus, but those predictions were more often correct. When predicting transposable elements, RNNotate was more accurate than both Repeat-Masker and RepeatScout. Additionally, we found that RNNotate was significantly less sensitive when trained and run without DNA methylation, validating our hypothesis. To our best knowledge, we are not only the first group to use recurrent neural networks for eukaryotic genome annotation, but we also innovated in the data space by utilizing DNA methylation patterns for prediction

Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark

The trend for listening to music online has greatly increased over the past decade due to the number of online musical tracks. The large music databases of music libraries that are provided by online music content distribution vendors make music streaming and downloading services more accessible to the end-user. It is essential to classify similar types of songs with an appropriate tag or index (genre) to present similar songs in a convenient way to the end-user. As the trend of online music listening continues to increase, developing multiple machine learning models to classify music genres has become a main area of research. In this research paper, a popular music dataset GTZAN which contains ten music genres is analysed to study various types of music features and audio signals. Multiple scalable machine learning algorithms supported by Apache Spark, including naïve Bayes, decision tree, logistic regression, and random forest, are investigated for the classification of music genres. The performance of these classifiers is compared, and the random forest performs as the best classifier for the classification of music genres. Apache Spark is used in this paper to reduce the computation time for machine learning predictions with no computational cost, as it focuses on parallel computation. The present work also demonstrates that the perfect combination of Apache Spark and machine learning algorithms reduces the scalability problem of the computation of machine learning predictions. Moreover, different hyperparameters of the random forest classifier are optimized to increase the performance efficiency of the classifier in the domain of music genre classification. The experimental outcome shows that the developed random forest classifier can establish a high level of performance accuracy, especially for the mislabelled, distorted GTZAN dataset. This classifier has outperformed other machine learning classifiers supported by Apache Spark in the present work. The random forest classifier manages to achieve 90% accuracy for music genre classification compared to other work in the same domain

Big data analytics: a predictive analysis applied to cybersecurity in a financial organization

Project Work presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceWith the generalization of the internet access, cyber attacks have registered an alarming growth in frequency and severity of damages, along with the awareness of organizations with heavy investments in cybersecurity, such as in the financial sector. This work is focused on an organization’s financial service that operates on the international markets in the payment systems industry. The objective was to develop a predictive framework solution responsible for threat detection to support the security team to open investigations on intrusive server requests, over the exponentially growing log events collected by the SIEM from the Apache Web Servers for the financial service. A Big Data framework, using Hadoop and Spark, was developed to perform classification tasks over the financial service requests, using Neural Networks, Logistic Regression, SVM, and Random Forests algorithms, while handling the training of the imbalance dataset through BEV. The main conclusions over the analysis conducted, registered the best scoring performances for the Random Forests classifier using all the preprocessed features available. Using the all the available worker nodes with a balanced configuration of the Spark executors, the most performant elapsed times for loading and preprocessing of the data were achieved using the column-oriented ORC with native format, while the row-oriented CSV format performed the best for the training of the classifiers.Com a generalização do acesso à internet, os ciberataques registaram um crescimento alarmante em frequência e severidade de danos causados, a par da consciencialização das organizações, com elevados investimentos em cibersegurança, como no setor financeiro. Este trabalho focou-se no serviço financeiro de uma organização que opera nos mercados internacionais da indústria de sistemas de pagamento. O objetivo consistiu no desenvolvimento uma solução preditiva responsável pela detecção de ameaças, por forma a dar suporte à equipa de segurança na abertura de investigações sobre pedidos intrusivos no servidor, relativamente aos exponencialmente crescentes eventos de log coletados pelo SIEM, referentes aos Apache Web Servers, para o serviço financeiro. Uma solução de Big Data, usando Hadoop e Spark, foi desenvolvida com o objectivo de executar tarefas de classificação sobre os pedidos do serviço financeiros, usando os algoritmos Neural Networks, Logistic Regression, SVM e Random Forests, solucionando os problemas associados ao treino de um dataset desequilibrado através de BEV. As principais conclusões sobre as análises realizadas registaram os melhores resultados de classificação usando o algoritmo Random Forests com todas as variáveis pré-processadas disponíveis. Usando todos os nós do cluster e uma configuração balanceada dos executores do Spark, os melhores tempos para carregar e pré-processar os dados foram obtidos usando o formato colunar ORC nativo, enquanto o formato CSV, orientado a linhas, apresentou os melhores tempos para o treino dos classificadores

OntoTouTra: tourist traceability ontology based on big data analytics

Tourist traceability is the analysis of the set of actions, procedures, and technical measures that allows us to identify and record the space–time causality of the tourist’s touring, from the beginning to the end of the chain of the tourist product. Besides, the traceability of tourists has implications for infrastructure, transport, products, marketing, the commercial viability of the industry, and the management of the destination’s social, environmental, and cultural impact. To this end, a tourist traceability system requires a knowledge base for processing elements, such as functions, objects, events, and logical connectors among them. A knowledge base provides us with information on the preparation, planning, and implementation or operation stages. In this regard, unifying tourism terminology in a traceability system is a challenge because we need a central repository that promotes standards for tourists and suppliers in forming a formal body of knowledge representation. Some studies are related to the construction of ontologies in tourism, but none focus on tourist traceability systems. For the above, we propose OntoTouTra, an ontology that uses formal specifications to represent knowledge of tourist traceability systems. This paper outlines the development of the OntoTouTra ontology and how we gathered and processed data from ubiquitous computing using Big Data analysis techniquesThis research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja

Data Science for Finance: Targeted Learning from (Big) Data to Economic Stability and Financial Risk Management

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsThe modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed situations, financial crises, and pandemics are the source of many new risks with new requirements such as new data sources and different models. This dissertation aims to show the data quality challenges of high-risk situations such as pandemics or economic crisis and it try to theorize the new machine learning models for predictive and longitudes time series models. In the first study (Chapter Two) we analyzed and compared the quality of official datasets available for COVID-19 as a best practice for a recent high-risk situation with dramatic effects on financial stability. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organizations based on the value of systematic measurement errors. We combined excel files, text mining techniques, and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data. In the second study (Chapter Three) we discussed credit risk as the most significant source of risk in banking as one of the most important sectors of financial institutions. We proposed a new machine learning approach for online credit scoring which is enough conservative and robust for unstable and high-risk situations. This Chapter is aimed at the case of credit scoring in risk management and presents a novel method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study its impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which can reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability. In the third study (Chapter Four) we focus on the financial stability challenges faced by insurance companies and pension schemes when managing systematic (undiversifiable) mortality and longevity risk. For this purpose, we first developed a new ensemble learning strategy for panel time-series forecasting and studied its applications to tracking respiratory disease excess mortality during the COVID-19 pandemic. The layered learning approach is a solution related to ensemble learning to address a given predictive task by different predictive models when direct mapping from inputs to outputs is not accurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, the appropriate holdout for each model is specified individually. Additionally, the models in the ensemble are selected by a proposed selection approach to be combined dynamically based on their predictive performance. It provides a high-performance ensemble model to automatically cope with the different kinds of time series for each panel member. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series (countries) of reported respiratory disease deaths with monthly sampling frequency to show the amount of improvement in predictive performance. We then compare each country’s forecasts of respiratory disease deaths generated by our model with the corresponding COVID-19 deaths in 2020. The results of this large set of experiments show that the accuracy of the ensemble model is improved noticeably by using different holdouts for different contributed time series methods based on the proposed model selection method. These improved time series models provide us proper forecasting of respiratory disease deaths for each country, exhibiting high correlation (0.94) with Covid-19 deaths in 2020. In the fourth study (Chapter Five) we used the new ensemble learning approach for time series modeling, discussed in the previous Chapter, accompany by K-means clustering for forecasting life tables in COVID-19 times. Stochastic mortality modeling plays a critical role in public pension design, population and public health projections, and in the design, pricing, and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast the mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this Chapter, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends, and use ensemble learning to forecast life expectancy and annuity prices by age and sex. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19. In this dissertation’s conclusions (Chapter Six), we provide more detailed insights about the overall contributions of this dissertation on the financial stability and risk management by data science, opportunities, limitations, and avenues for future research about the application of data science in finance and economy

Music similarity analysis using the big data framework spark

A parameterizable recommender system based on the Big Data processing framework Spark is introduced, which takes multiple tonal properties of music into account and is capable of recommending music based on a user's personal preferences. The implemented system is fully scalable; more songs can be added to the dataset, the cluster size can be increased, and the possibility to add different kinds of audio features and more state-of-the-art similarity measurements is given. This thesis also deals with the extraction of the required audio features in parallel on a computer cluster. The extracted features are then processed by the Spark based recommender system, and song recommendations for a dataset consisting of approximately 114000 songs are retrieved in less than 12 seconds on a 16 node Spark cluster, combining eight different audio feature types and similarity measurements.Ein parametrisierbares Empfehlungssystem, basierend auf dem Big Data Framework Spark, wird präsentiert. Dieses berücksichtigt verschiedene klangliche Eigenschaften der Musik und erstellt Musikempfehlungen basierend auf den persönlichen Vorlieben eines Nutzers. Das implementierte Empfehlungssystem ist voll skalierbar. Mehr Lieder können dem Datensatz hinzugefügt werden, mehr Rechner können in das Computercluster eingebunden werden und die Möglichkeit andere Audiofeatures und aktuellere Ähnlichkeitsmaße hizuzufügen und zu verwenden, ist ebenfalls gegeben. Des Weiteren behandelt die Arbeit die parallele Berechnung der benötigten Audiofeatures auf einem Computercluster. Die Features werden von dem auf Spark basierenden Empfehlungssystem verarbeitet und Empfehlungen für einen Datensatz bestehend aus ca. 114000 Liedern können unter Berücksichtigung von acht verschiedenen Arten von Audiofeatures und Abstandsmaßen innerhalb von zwölf Sekunden auf einem Computercluster mit 16 Knoten berechnet werden

MULTI-TASK NEURAL NETWORK MODEL FOR CUSTOMER SERVICE EXPERIENCE PREDICTION ANALYSIS BASED IN QOS AND QOE DATA, OVER TELECOMMUNICATIONS INTERNET AND TV SERVICES

Telecommunication companies monitor the infrastructure and equipment to guarantee the quality delivery of their services to the customer. Customer experience is of utmost importance in the telecommunication area due to the fierce market competition. There are two main reasons for a client to experience bad quality of service: disruption events and service degradation. Disruptive events map to situations where the client loses complete access to the service, whereas service degradation limits the client’s service and/or can origin multiple failures during the service use. In disruption situations, the client will immediately call and make a complaint. In degradation the client may not complain due to many different reasons, but mainly because the client does not use the service or does not want to go through the process of calling the operator to place a complaint. This dissertation presents a solution that can identify customers with degradation in their services so that the company can proactively call the signaled customers and sched- ule an intervention to correct the problems. Currently is suspected that the intersection between the services is higher than is perceived, so it is the thesis objective to explore and verify services correlation. To achieve the goal we start by characterizing the problem, then explore the real data consumption patterns, and analyze the intersection in experi- ence problems. To cope with TV and Internet services complains target simultaneously the thesis leverages a model with a multi-task architecture whose goal is to learn the implicit information available in the services intersection. Multi-task model solves the limitations existent in current solutions and adds the possibility to have multiple targets that share information between them. Then, the model develops the capacity to learn situations where the symptom and the resolution are in different services. Through the multi-task model capacity to explore the implicit information contained in the services intersection the results are improved 25% against the benchmark models which do not support multi-task. Given the performance increase, the company project strategy was impacted and new uses cases considered.As empresas de telecomunicações monitorizam a infraestrutura e os equipamentos para conseguirem garantir qualidade na entrega dos serviços aos clientes. Num mercado feroz como o de telecomunicações, investir na experiência de utilizador é muito importante. As duas razões principais para explicar má experiencia de serviço são: eventos disruptivos e degradação no serviço. Nos eventos disruptivos o cliente perde totalmente o acesso ao serviço, enquanto que para problemas de degradação o serviço fica limitado e/ou pode falhar durante a utilização do mesmo. Em situações disruptivas, o cliente contacta immediatamente a empresa para apreentar uma queixa. Já nos casos de degradação o cliente pode não se queixar. Entre muitas razões possiveis sobressaiem as seguintes, o cliente não usa o serviço e o cliente prefere não passar pelo processo duro de contactar o operador para se queixar do mau serviço. A dissertação implementa uma solução que identifica clientes com degradação no serviço, para que a empresa possa ter uma atitude proactiva em relação aos clientes sinalizados, e consiga marcar intervenções técnicas para resolver o problema. A tese tem como objectivo validar que a interseção entre os serviços de TV e Internet é maior do que atualmente reconhecido, e que a interseção produz informação que deve ser explorada. No primeiro exercicio caracterizamos do problema, exploramos os dados reais de padrões de consumo, e é analisada a situação de interseção entre os serviços. Desenvolvemos o modelo segundo um arquitetura multi-task que permite a partilha de informação e portanto o modelo aprende a informação implícita que existe na interseção do serviços. O modelo multi-task corrige as limitações existentes nos modelos atuais, e permite a integração de outros problemas que a empresa venha a sentir necessidade de resolver. Os resultados obtidos quando comparados com os modelos de benchmark cuja ar- quitetura não suporta multi-task mostram um aumento de performance acima dos 25%. Devido aos resultados obtidos a estratégia para o projeto foi revista e novas ideias de projetos surgiram

The Design and Implementation of Low-Latency Prediction Serving Systems

Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application. However, most machine learning frameworks and systems are optimized for model training and not deployment.In this thesis, I discuss three prediction serving systems designed to meet the needs of modern interactive machine learning applications. The key idea in this work is to utilize a decoupled, layered design that interposes systems on top of training frameworks to build low-latency, scalable serving systems. Velox introduced this decoupled architecture to enable fast online learning and model personalization in response to feedback. Clipper generalized this system architecture to be framework-agnostic and introduced a set of optimizations to reduce and bound prediction latency and improve prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. And InferLine provisions and manages the individual stages of prediction pipelines to minimize cost while meeting end-to-end tail latency constraints

B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project