| 1. |
- Herman, Stephanie
(författare)
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Towards an Earlier Detection of Progressive Multiple Sclerosis using Metabolomics and Machine Learning
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Decision-making guided by advanced analytics is becoming increasingly common in many fields. Implementing computationally driven healthcare solutions does, however, pose ethical dilemmas as it involves human health. Therefore, augmenting clinical expertise with advanced analytical insights to support decision-making in healthcare is probably a more feasible strategy.Multiple sclerosis is a debilitating neurological disease with two subtypes; relapsing-remitting multiple sclerosis (RRMS) and the typically late-stage progressive subtype (PMS). Progressive multiple sclerosis is a neurodegenerative phenotype, with a vague functional definition, that currently is diagnosed retrospectively. The challenge of diagnosing PMS earlier is a great example where data-driven insights might prove useful.This thesis addresses the need for an earlier detection of patients developing the progressive and neurodegenerative subtype of multiple sclerosis, using primarily metabolomics and machine learning approaches. In Paper I, the biochemical differences in cerebrospinal fluid (CSF) from RRMS and PMS patients were characterised, leading to the conclusion that it is possible to distinguish PMS patients based on biochemical alterations. In addition, pathway analysis revealed several metabolic pathways that were affected in the transition to PMS, including tryptophan metabolism and pyrimidine metabolism. In Paper II and III, the possibility of generating a concise PMS signature based on solely low-molecular measurements (III) or in combination with radiological and protein measures (II) was explored. In both cases, it was concluded that it is plausible to generate a condensed set of highly informative markers that can distinguish PMS patients from RRMS patients. In Paper III, the classifier was complemented with conformal prediction that enabled an estimate of confidence in single patient predictions and a personalised evaluation of current disease state. Finally, in Paper IV, the extracted low-molecular marker candidates were characterised in isolation, revealing that several metabolites were distinctively altered in the CSF of PMS patients, including increased levels of 4-acetamidobutanoate, 4-hydroxybenzoate and thymine.Overall, the results from this work indicate that it is possible to detect PMS at an earlier stage and that advanced analytical algorithms can support healthcare.
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| 2. |
- Capuccini, Marco
(författare)
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Enabling Scalable Data Analysis on Cloud Resources with Applications in Life Science
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Over the past 20 years, the rise of high-throughput methods in life science has enabled research laboratories to produce massive datasets of biological interest. When dealing with this "data deluge" of modern biology researchers encounter two major challenges: first, there is a need for substantial technical skills for dealing with Big Data and; second, infrastructure procurement becomes difficult. In connection to this second challenge, the computing model and business trend that was originally popularized by Amazon under the name of cloud computing represents an interesting opportunity. Instead of buying computing infrastructure upfront, cloud providers enable the allocation and release of virtual resources on-demand. These resources are then billed with a pay-per-use pricing model and physical infrastructure management is delegated to the provider. In this thesis, we introduce a number of methods for running Big Data analyses of biological interest using cloud computing. Considerable efforts were made in enabling the application of trusted, bioinformatics software to Big Data scenarios as opposed to reimplementing the existing codebase. Further, we improve the accessibility of the technology with the aim of reducing the entry barrier for biologists. The thesis includes 5 papers. In Papers I and II, we explore the applicability of Apache Spark, one of the leading Big Data analytics platforms in cloud environments, to two drug-discovery use cases. In Paper III, we present a general method for running bioinformatics analyses on the cloud using the microservices-oriented architecture. In Paper IV, we introduce a method that combines microservices and Apache Spark with the aim of providing the best of both technologies. In Paper V, we discuss how to reduce the entry barrier for the allocation of cloud research environments. We show that all of the developed methods scale well and we provide high-level programming interfaces for improving accessibility. We have also made the developed software publicly available.
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| 3. |
- Ahmed, Laeeq, et al.
(författare)
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Predicting target profiles with confidence as a service using docking scores
- 2020
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Ingår i: Journal of Cheminformatics. - : Springer Nature. - 1758-2946. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues. Contributions: We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis. Results: The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.
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| 4. |
- Capuccini, Marco, et al.
(författare)
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MaRe : Processing Big Data with application containers on Apache Spark
- 2020
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Ingår i: GigaScience. - : Oxford University Press. - 2047-217X. ; 9:5
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Tidskriftsartikel (refereegranskat)abstract
- Background: Life science is increasingly driven by Big Data analytics, and the MapReduce programming model has been proven successful for data-intensive analyses. However, current MapReduce frameworks offer poor support for reusing existing processing tools in bioinformatics pipelines. Furthermore, these frameworks do not have native support for application containers, which are becoming popular in scientific data processing. Results: Here we present MaRe, an open source programming library that introduces support for Docker containers in Apache Spark. Apache Spark and Docker are the MapReduce framework and container engine that have collected the largest open source community; thus, MaRe provides interoperability with the cutting-edge software ecosystem. We demonstrate MaRe on 2 data-intensive applications in life science, showing ease of use and scalability. Conclusions: MaRe enables scalable data-intensive processing in life science with Apache Spark and application containers. When compared with current best practices, which involve the use of workflow systems, MaRe has the advantage of providing data locality, ingestion from heterogeneous storage systems, and interactive processing. MaRe is generally applicable and available as open source software.
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| 5. |
- Capuccini, Marco, et al.
(författare)
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On-demand virtual research environments using microservices
- 2019
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Ingår i: PeerJ Computer Science. - : PeerJ. - 2376-5992. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the degree of flexibility required by the scientific community. Here we present a microservice-oriented methodology, where scientific applications run in a distributed orchestration platform as software containers, referred to as on-demand, virtual research environments. The methodology is vendor agnostic and we provide an open source implementation that supports the major cloud providers, offering scalable management of scientific pipelines. We demonstrate applicability and scalability of our methodology in life science applications, but the methodology is general and can be applied to other scientific domains.
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| 6. |
- Dahlö, Martin, et al.
(författare)
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Tracking the NGS revolution : managing life science research on shared high-performance computing clusters
- 2018
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Ingår i: GigaScience. - : Oxford University Press. - 2047-217X. ; 7:5
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundNext-generation sequencing (NGS) has transformed the life sciences, and many research groups are newly dependent upon computer clusters to store and analyze large datasets. This creates challenges for e-infrastructures accustomed to hosting computationally mature research in other sciences. Using data gathered from our own clusters at UPPMAX computing center at Uppsala University, Sweden, where core hour usage of ∼800 NGS and ∼200 non-NGS projects is now similar, we compare and contrast the growth, administrative burden, and cluster usage of NGS projects with projects from other sciences.ResultsThe number of NGS projects has grown rapidly since 2010, with growth driven by entry of new research groups. Storage used by NGS projects has grown more rapidly since 2013 and is now limited by disk capacity. NGS users submit nearly twice as many support tickets per user, and 11 more tools are installed each month for NGS projects than for non-NGS projects. We developed usage and efficiency metrics and show that computing jobs for NGS projects use more RAM than non-NGS projects, are more variable in core usage, and rarely span multiple nodes. NGS jobs use booked resources less efficiently for a variety of reasons. Active monitoring can improve this somewhat.ConclusionsHosting NGS projects imposes a large administrative burden at UPPMAX due to large numbers of inexperienced users and diverse and rapidly evolving research areas. We provide a set of recommendations for e-infrastructures that host NGS research projects. We provide anonymized versions of our storage, job, and efficiency databases.
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| 7. |
- Emami Khoonsari, Payam, et al.
(författare)
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Interoperable and scalable data analysis with microservices : Applications in metabolomics
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 35:19, s. 3752-3760
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Tidskriftsartikel (refereegranskat)abstract
- MotivationDeveloping a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator.ResultsWe developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.
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| 8. |
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| 9. |
- Gauraha, Niharika, et al.
(författare)
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Robust Knowledge Transfer in Learning Under Privileged Information Framework
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Learning Under Privileged Information (LUPI) enables the inclusion of additional (privileged) information when training machine learning models; data that is not available when making predictions. The methodology has been successfully applied to a diverse set of problems from various fields. SVM+ was the first realization of the LUPI paradigm which showed fast convergence but did not scale well. To address the scalability issue, knowledge transfer approaches were proposed to estimate privileged information from standard features in order to construct improved decision rules.Most available knowledge transfer methods use regression techniques and the same data for approximating the privileged features as for learning the transfer function.Inspired by the cross-validation approach, we propose to partition the training data into K folds and use each fold for learning a transfer function and the remaining folds for approximations of privileged features - we refer to this a robust knowledge transfer. We conduct empirical evaluation considering four different experimental setups using one synthetic and three real datasets. These experiments demonstrate that our approach yields improved accuracy as compared to LUPI with standard knowledge transfer.
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| 10. |
- Gauraha, Niharika, et al.
(författare)
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Split knowledge transfer in learning under privileged information framework
- 2019
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Ingår i: Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications. - : PMLR. ; , s. 43-52
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Konferensbidrag (refereegranskat)abstract
- Learning Under Privileged Information (LUPI) enables the inclusion of additional (privileged) information when training machine learning models, data that is not available when making predictions. The methodology has been successfully applied to a diverse set of problems from various fields. SVM+ was the first realization of the LUPI paradigm which showed fast convergence but did not scale well. To address the scalability issue, knowledge transfer approaches were proposed to estimate privileged information from standard features in order to construct improved decision rules. Most available knowledge transfer methods use regression techniques and the same data for approximating the privileged features as for learning the transfer function. Inspired by the cross-validation approach, we propose to partition the training data into $K$ folds and use each fold for learning a transfer function and the remaining folds for approximations of privileged features—we refer to this as split knowledge transfer. We evaluate the method using four different experimental setups comprising one synthetic and three real datasets. The results indicate that our approach leads to improved accuracy as compared to LUPI with standard knowledge transfer.
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