Evaluating FAIR Digital Object and Linked Data as distributed object systems.

FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building an ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes.

BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs.

Background: Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs.

Relational graph convolutional networks: a closer look.

In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowledge Graph datasets on node classification and link prediction tasks.

Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation.

Background: Electronic Laboratory Notebooks (ELNs) are used to document experiments and investigations in the wet-lab. Protocols in ELNs contain a detailed description of the conducted steps including the necessary information to understand the procedure and the raised research data as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create semantic documentation of the provenance of research data by the use of ontologies and linked data methodologies.

The non-linear impact of data handling on network diffusion models.

Many computational models rely on real-world data, and the steps required in moving from data collection, to data preparation, to model calibration, and input are becoming increasingly complex. Errors in data can lead to errors in model output that might invalidate conclusions in extreme cases. While the challenge of errors in data collection have been analyzed in the literature, here we highlight the importance of data handling in the modeling and simulation process, and how particular data handling errors can lead to errors in model output.

Perspectives on automated composition of workflows in the life sciences.

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus.

Dataset Reuse: Toward Translating Principles to Practice.

The web provides access to millions of datasets that can have additional impact when used beyond their original context. We have little empirical insight into what makes a dataset more reusable than others and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data.

MTH1 Inhibitor TH588 Disturbs Mitotic Progression and Induces Mitosis-Dependent Accumulation of Genomic 8-oxodG.

Reactive oxygen species (ROS) oxidize nucleotide triphosphate pools (e.g., 8-oxodGTP), which may kill cells if incorporated into DNA.

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines.

A cross-disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines. Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

Targeting PFKFB3 radiosensitizes cancer cells and suppresses homologous recombination.

The glycolytic PFKFB3 enzyme is widely overexpressed in cancer cells and an emerging anti-cancer target. Here, we identify PFKFB3 as a critical factor in homologous recombination (HR) repair of DNA double-strand breaks. PFKFB3 rapidly relocates into ionizing radiation (IR)-induced nuclear foci in an MRN-ATM-γH2AX-MDC1-dependent manner and co-localizes with DNA damage and HR repair proteins.

Indicators for the use of robotic labs in basic biomedical research: a literature analysis.

Robotic labs, in which experiments are carried out entirely by robots, have the potential to provide a reproducible and transparent foundation for performing basic biomedical laboratory experiments. In this article, we investigate whether these labs could be applicable in current experimental practice. We do this by text mining 1,628 papers for occurrences of methods that are supported by commercial robotic labs.

Distinct mechanistic responses to replication fork stalling induced by either nucleotide or protein deprivation.

Incidents that slow or stall replication fork progression, collectively known as replication stress, represent a major source of spontaneous genomic instability. Here, we determine the requirement for global protein biosynthesis on DNA replication and associated downstream signaling. We study this response side by side with dNTP deprivation; one of the most commonly used means to investigate replication arrest and replicative stress.

The health care and life sciences community profile for dataset descriptions.

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories.

Knockdown of platinum-induced growth differentiation factor 15 abrogates p27-mediated tumor growth delay in the chemoresistant ovarian cancer model A2780cis.

Molecular mechanisms underlying the development of resistance to platinum-based treatment in patients with ovarian cancer remain poorly understood. This is mainly due to the lack of appropriate in vivo models allowing the identification of resistance-related factors. In this study, we used human whole-genome microarrays and linear model analysis to identify potential resistance-related genes by comparing the expression profiles of the parental human ovarian cancer model A2780 and its platinum-resistant variant A2780cis before and after carboplatin treatment in vivo.

BRCA2 and RAD51 promote double-strand break formation and cell death in response to gemcitabine.

Replication inhibitors cause replication fork stalling and double-strand breaks (DSB) that result from processing of stalled forks. During recovery from replication blocks, the homologous recombination (HR) factor RAD51 mediates fork restart and DSB repair. HR defects therefore sensitize cells to replication inhibitors, with clear implications for cancer therapy.

Prediction errors in learning drug response from gene expression data - influence of labeling, sample size, and machine learning algorithm.

Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response.

Genome Fusion Detection: a novel method to detect fusion genes from SNP-array data.

Motivation: Fusion genes result from genomic rearrangements, such as deletions, amplifications and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur.

DNA interstrand crosslinks induce a potent replication block followed by formation and repair of double strand breaks in intact mammalian cells.

DNA interstrand crosslinks (ICLs) are highly toxic lesions that covalently link both strands of DNA and distort the DNA helix. Crosslinking agents have been shown to stall DNA replication and failure to repair ICL lesions before encountered by replication forks may induce severe DNA damage. Most knowledge of the ICL repair process has been revealed from studies in bacteria and cell extracts.