Transforming the study of organisms: Phenomic data models and knowledge bases

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

An atlas of healthy and injured cell states and niches in the human kidney

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhood

A provenance-based semantic approach to support understandability, reproducibility, and reuse of scientific experiments

Understandability and reproducibility of scientific results are vital in every field of science. Several reproducibility measures are being taken to make the data used in the publications findable and accessible. However, there are many challenges faced by scientists from the beginning of an experiment to the end in particular for data management. The explosive growth of heterogeneous research data and understanding how this data has been derived is one of the research problems faced in this context. Interlinking the data, the steps and the results from the computational and non-computational processes of a scientific experiment is important for the reproducibility. We introduce the notion of end-to-end provenance management'' of scientific experiments to help scientists understand and reproduce the experimental results. The main contributions of this thesis are: (1) We propose a provenance modelREPRODUCE-ME'' to describe the scientific experiments using semantic web technologies by extending existing standards. (2) We study computational reproducibility and important aspects required to achieve it. (3) Taking into account the REPRODUCE-ME provenance model and the study on computational reproducibility, we introduce our tool, ProvBook, which is designed and developed to demonstrate computational reproducibility. It provides features to capture and store provenance of Jupyter notebooks and helps scientists to compare and track their results of different executions. (4) We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) for the end-to-end provenance management. This collaborative framework allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational steps in an interoperable way. We apply our contributions to a set of scientific experiments in microscopy research projects

Text Mining and Gene Expression Analysis Towards Combined Interpretation of High Throughput Data

Microarrays can capture gene expression activity for thousands of genes simultaneously and thus make it possible to analyze cell physiology and disease processes on molecular level. The interpretation of microarray gene expression experiments profits from knowledge on the analyzed genes and proteins and the biochemical networks in which they play a role. The trend is towards the development of data analysis methods that integrate diverse data types. Currently, the most comprehensive biomedical knowledge source is a large repository of free text articles. Text mining makes it possible to automatically extract and use information from texts. This thesis addresses two key aspects, biomedical text mining and gene expression data analysis, with the focus on providing high-quality methods and data that contribute to the development of integrated analysis approaches. The work is structured in three parts. Each part begins by providing the relevant background, and each chapter describes the developed methods as well as applications and results. Part I deals with biomedical text mining: Chapter 2 summarizes the relevant background of text mining; it describes text mining fundamentals, important text mining tasks, applications and particularities of text mining in the biomedical domain, and evaluation issues. In Chapter 3, a method for generating high-quality gene and protein name dictionaries is described. The analysis of the generated dictionaries revealed important properties of individual nomenclatures and the used databases (Fundel and Zimmer, 2006). The dictionaries are publicly available via a Wiki, a web service, and several client applications (Szugat et al., 2005). In Chapter 4, methods for the dictionary-based recognition of gene and protein names in texts and their mapping onto unique database identifiers are described. These methods make it possible to extract information from texts and to integrate text-derived information with data from other sources. Three named entity identification systems have been set up, two of them building upon the previously existing tool ProMiner (Hanisch et al., 2003). All of them have shown very good performance in the BioCreAtIvE challenges (Fundel et al., 2005a; Hanisch et al., 2005; Fundel and Zimmer, 2007). In Chapter 5, a new method for relation extraction (Fundel et al., 2007) is presented. It was applied on the largest collection of biomedical literature abstracts, and thus a comprehensive network of human gene and protein relations has been generated. A classification approach (Küffner et al., 2006) can be used to specify relation types further; e. g., as activating, direct physical, or gene regulatory relation. Part II deals with gene expression data analysis: Gene expression data needs to be processed so that differentially expressed genes can be identified. Gene expression data processing consists of several sequential steps. Two important steps are normalization, which aims at removing systematic variances between measurements, and quantification of differential expression by p-value and fold change determination. Numerous methods exist for these tasks. Chapter 6 describes the relevant background of gene expression data analysis; it presents the biological and technical principles of microarrays and gives an overview of the most relevant data processing steps. Finally, it provides a short introduction to osteoarthritis, which is in the focus of the analyzed gene expression data sets. In Chapter 7, quality criteria for the selection of normalization methods are described, and a method for the identification of differentially expressed genes is proposed, which is appropriate for data with large intensity variances between spots representing the same gene (Fundel et al., 2005b). Furthermore, a system is described that selects an appropriate combination of feature selection method and classifier, and thus identifies genes which lead to good classification results and show consistent behavior in different sample subgroups (Davis et al., 2006). The analysis of several gene expression data sets dealing with osteoarthritis is described in Chapter 8. This chapter contains the biomedical analysis of relevant disease processes and distinct disease stages (Aigner et al., 2006a), and a comparison of various microarray platforms and osteoarthritis models. Part III deals with integrated approaches and thus provides the connection between parts I and II: Chapter 9 gives an overview of different types of integrated data analysis approaches, with a focus on approaches that integrate gene expression data with manually compiled data, large-scale networks, or text mining. In Chapter 10, a method for the identification of genes which are consistently regulated and have a coherent literature background (Küffner et al., 2005) is described. This method indicates how gene and protein name identification and gene expression data can be integrated to return clusters which contain genes that are relevant for the respective experiment together with literature information that supports interpretation. Finally, in Chapter 11 ideas on how the described methods can contribute to current research and possible future directions are presented

Towards Machine-Assisted Meta Studies of Astrophysical Data From the Scientific Literature

We develop a new model for automatic extraction of reported measurements from the astrophysical literature, utilising modern Natural Language Processing techniques. We begin with a rules-based model for keyword-search-based extraction, and then proceed to develop artificial neural network models for full entity and relation extraction from free text. This process also requires the creation of hand-annotated datasets selected from the available astrophysical literature for training and validation purposes. We use a set of cosmological parameters to examine the model's ability to identify information relating to a specific parameter and to illustrate its capabilities, using the Hubble constant as a primary case study due to the well-document history of that parameter. Our results correctly highlight the current tension present in measurements of the Hubble constant and recover the 3.5σ discrepancy – demonstrating that the models are useful for meta-studies of astrophysical measurements from a large number of publications. From the other cosmological parameter results we can clearly observe the historical trends in the reported values of these quantities over the past two decades, and see the impacts of landmark publications on our understanding of cosmology. The outputs of these models, when applied to the article abstracts present in the arXiv repository, constitute a database of over 231,000 astrophysical numerical measurements, relating to over 61,000 different symbolic parameter representations – here a measurement refers to the combination of a numerical value and an identifier (i.e. a name or symbol) to give it physical meaning. We present an online interface (Numerical Atlas) to allow users to query and explore this database, based on parameter names and symbolic representations, and download the resulting datasets for their own research uses

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

The Gene Ontology Handbook

bioinformatics; biotechnolog

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp