Top 10 metrics for life science software good practices [version 1; referees: 2 approved]

Metrics for assessing adoption of good development practices are a useful way to ensure that software is sustainable, reusable and functional. Sustainability means that the software used today will be available - and continue to be improved and supported - in the future. We report here an initial set of metrics that measure good practices in software development. This initiative differs from previously developed efforts in being a community-driven grassroots approach where experts from different organisations propose good software practices that have reasonable potential to be adopted by the communities they represent. We not only focus our efforts on understanding and prioritising good practices, we assess their feasibility for implementation and publish them here

A framework to assess the quality and impact of bioinformatics training across ELIXIR.

ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course

Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR\u27s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives

The ELIXIR Human Copy Number Variations Community:building bioinformatics infrastructure for research

Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While 'High-Throughput' sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR's recently established h uman CNV Community, with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context

Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning

<div><p>Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions. We represent DDIs as a complex network in which nodes refer to drugs and links refer to their potential interactions. Recently, the problem of link prediction has attracted much consideration in scientific community. We represent the process of link prediction as a binary classification task on networks of potential DDIs. We use link prediction techniques for predicting unknown interactions between drugs in five arbitrary chosen large-scale DDI databases, namely DrugBank, KEGG, NDF-RT, SemMedDB, and Twosides. We estimated the performance of link prediction using a series of experiments on DDI networks. We performed link prediction using unsupervised and supervised approach including classification tree, <i>k</i>-nearest neighbors, support vector machine, random forest, and gradient boosting machine classifiers based on topological and semantic similarity features. Supervised approach clearly outperforms unsupervised approach. The Twosides network gained the best prediction performance regarding the area under the precision-recall curve (0.93 for both random forests and gradient boosting machine). The applied methodology can be used as a tool to help researchers to identify potential DDIs. The supervised link prediction approach proved to be promising for potential DDIs prediction and may facilitate the identification of potential DDIs in clinical research.</p></div

THE USE OF HORMONE REPLACEMENT THERAPY IN SLOVENIAN WOMEN BEFORE THE FIRST DIAGNOSIS OF BREAST CANCER

Background. The aim of our retrospective study was to collect and compare data on hormone replacement therapy prescription in Slovenian women before the first diagnosis of breast cancer and the control group of randomly selected healthy women matched by age. Patients and methods. We carried out a cross-sectional, case-control study and enrolled 1408 women aged between  50 and 69 years. They were invited to participate via a personal letter and asked to complete written questionnaire. Besides questions regarding drug intake of sex hormones and general information, questionnaire contained questions that provide reproductive data and family history of cancer. We used the independent t-test to compare the values of the means between the two groups and the chi-square statistic to determine an association for categorical data between groups. Results. In our study, significantly more women in the control group were using HRT. Although, there was not statistically significant difference in the proportion of women between the control and the experimental group using HRT for more than 5 years. There was higher proportion of women in the experimental group receiving combined HRT, but the difference was not statistically significant. Furthermore, there was also significantly higher proportion of women in the experimental group receiving systemic HRT and significantly higher proportion of women in the control group receiving local HRT preparations. Conclusions. HRT is still the most efficient way to treat debilitating menopausal symptoms. Although the linkage between the use of HRT and increased breast cancer risk is physiologically plausible, our preliminary results show that observable risk is moderate

Classification performances for link prediction on test data.

<p>Classification performances for link prediction on test data.</p

Unsupervised classification performances for link prediction on training and test data.

<p>Unsupervised classification performances for link prediction on training and test data.</p

Critical difference (CD) plot for training data.

<p>Plot shows the pairwise differences in performance among classifiers. The horizontal scale shows the average rank of each classifier, with smaller ranks indicating better performance. Classifiers connected by a dark line had statistically identical performance at the <i>p</i> = 0.05 level.</p

Overlaps between data sources.

<p>Overlaps between data sources.</p