Networks are a common methodology used to capture increasingly complex associations between biological entities. They serve as a resource of biological knowledge for bioinformatics analyses, and also comprise the subsequent results. However, the interpretation of biological networks is challenging and requires suitable visualizations dependent on the contained information. The most prominent software in the field for the visualization of biological networks is Cytoscape, a desktop modeling environment also including many features for analysis. A further challenge when working with networks is their distribution. Within a typical collaborative workflow, even slight changes of the network data force one to repeat the visualization step as well. Also, just minor adjustments to the visual representation not only need the networks to be transferred back and forth. Collaboration on the same resources requires specific infrastructure to avoid redundancies, or worse, the corruption of the data. A well-established solution is provided by the NDEx platform where users can upload a network, share it with selected colleagues or make it publicly available. NDExEdit is a web-based application where simple changes can be made to biological networks within the browser, and which does not require installation. With our tool, plain networks can be enhanced easily for further usage in presentations and publications. Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. The web tool is designed to conform to the Cytoscape Exchange (CX) format as a data model, which is used for the data transmission by both tools, Cytoscape and NDEx. Therefore the modified network can be directly exported to the NDEx platform or saved as a compatible CX file, additionally to standard image formats like PNG and JPEG.

Background: Recent empirical studies indicate that online controlled experimentation is rarely systematically applied. A structured and complete experiment definition is the basis for systematic and trustworthy experimentation. Aims: As the first step towards guidelines for the definition of experiments, we explore experimentation application types that are conducted in practice. Additionally, we identify experiment definition characteristics that experts regard to considerable contribute to trustworthy experimentation. Method: An expert survey among fifteen industrial experts that published peer-reviewed publications was conducted. Results: In total, we identified fourteen types of applications with 32 concrete applications among the answers of the experts. The most frequently mentioned characteristics regarding the accuracy of an experiment were: success metrics, hypothesis, data quality metrics, guardrail metrics, alerting & shutdown, sizing, and segmentation. Conclusions: There are various applications for experimentation besides the ones mentioned in the literature. Most experts consider only about half of the known characteristics as relevant for the accuracy of an experiment.

Available platforms for online controlled experimentation primarily focus on the technical execution of experiments and are isolated from the remaining software development infrastructure. The platform-independent experimentation infrastructure separates the experiment definition from its execution and focuses on the experimentation process. However, it is still not integrated into the remaining infrastructure. In this paper, we extend the platform-independent experimentation infrastructure about interfaces to ease its integration into the software development infrastructure. The proposed solution is evaluated using a mixed-method research design to assess its usefulness, ease of use, strengths, and weaknesses. The results indicate that the proposed solution represents an adaptable, platform-independent, and cross-domain experimentation infrastructure that is perceived to be easy to use and useful.

Re-architecting monolithic systems with Microservices-based architecture is a common trend. Various companies are migrating to Microservices for different reasons. However, making such an important decision like re-architecting an entire system must be based on real facts and not only on gut feelings. The goal of this work is to propose an evidence-based decision support framework for companies that need to migrate to Microservices, based on the analysis of a set of characteristics and metrics they should collect before re-architecting their monolithic system. We conducted a survey done in the form of interviews with professionals to derive the assessment framework based on Grounded Theory. We identified a set consisting of information and metrics that companies can use to decide whether to migrate to Microservices or not. The proposed assessment framework, based on the aforementioned metrics, could be useful for companies if they need to migrate to Microservices and do not want to run the risk of failing to consider some important information.

Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve.

The definition of an experiment is a fundamental artifact of continuous experiments. It is used to formalize ideas as a plan of action and to document attained knowledge. Although there is a large body of research on continuous experimentation, the definition of experiments is not researched sufficiently. In this paper, we present a taxonomy of characteristics of continuous experiment definitions. The taxonomy is synthesized from a systematic literature review and a tool review of experimentation platforms. Our findings show amongst others that there are 17 characteristics of experiment definitions. Moreover, that platforms primarily focus on the execution of experiments and that experimentation is more than the execution of an experiment.

The systematic evaluation of ideas by experiments are the foundation of continuous experimentation. It allows to assess the value of an idea, remove guessing and subjective opinions from the discussion. The enormous interest of it by practitioners and researchers let the body of knowledge consistently grow. New framework, methods and techniques are developed and its application is constantly expanded to new fields like cyber-physical systems or social networks. In this paper we present a systematic mapping study to characterize the current state of research on continuous experimentation. Our study analyzes the following aspects: intensity of research activity and industry-academia collaboration, influential authors and publications, frequent research types and topics, kind of contributions and terms used for continuous experimentation. Our findings show amongst others that the intensity of research activities increases consistently, the collaboration between industry and academia is high and the most cited publications are experience reports from practitioners.

Motivation: Seamless exchange of biological network data enables bioinformatic algorithms to integrate networks as prior knowledge input as well as to document resulting network output. However, the interoperability between pathway databases and various methods and platforms for analysis is currently lacking. The Network Data Exchange (NDEx) is an open-source data commons that facilitates the user-centered sharing and publication of networks of many types and formats. Results: Here, we present a software package that allows users to programmatically connect to and interface with NDEx servers from within R. The network repository can be searched and networks can be retrieved and converted into igraph-compatible objects. These networks can be modified and extended within R and uploaded back to the NDEx servers. Availability and implementation: ndexr is a free and open-source R package, available via GitHub (https://github.com/frankkramer-lab/ndexr) and Bioconductor (http://bioconductor.org/packages/ndexr/). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference. METHODS: Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements. RESULTS AND CONCLUSIONS: During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA.