Goal-Based Selection of Visual Representations for Big Data Analytics

The H2020 TOREADOR Project adopts a model-driven architecture to streamline big data analytics and make it widely available to companies as a service. Our work in this context focuses on visualization, in particular on how to automate the translation of the visualization objectives declared by the user into a suitable visualization type. To this end we first define a visualization context based on seven prioritizable coordinates for assessing the user's objectives and describing the data to be visualized; then we propose a skyline-based technique for automatically translating a visualization context into a set of suitable visualization types. Finally, we evaluate our approach on a real use case excerpted from the pilot applications of TOREADOR

A computational exploration of resilience and evolvability of protein–protein interaction networks

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions , and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits

Strain-Dependent Differences in Bone Development, Myeloid Hyperplasia, Morbidity and Mortality in Ptpn2-Deficient Mice

Single nucleotide polymorphisms in the gene encoding the protein tyrosine phosphatase TCPTP (encoded by PTPN2) have been linked with the development of autoimmunity. Here we have used Cre/LoxP recombination to generate Ptpn2ex2−/ex2− mice with a global deficiency in TCPTP on a C57BL/6 background and compared the phenotype of these mice to Ptpn2−/− mice (BALB/c-129SJ) generated previously by homologous recombination and backcrossed onto the BALB/c background. Ptpn2ex2−/ex2− mice exhibited growth retardation and a median survival of 32 days, as compared to 21 days for Ptpn2−/− (BALB/c) mice, but the overt signs of morbidity (hunched posture, piloerection, decreased mobility and diarrhoea) evident in Ptpn2−/− (BALB/c) mice were not detected in Ptpn2ex2−/ex2− mice. At 14 days of age, bone development was delayed in Ptpn2−/− (BALB/c) mice. This was associated with increased trabecular bone mass and decreased bone remodeling, a phenotype that was not evident in Ptpn2ex2−/ex2− mice. Ptpn2ex2−/ex2− mice had defects in erythropoiesis and B cell development as evident in Ptpn2−/− (BALB/c) mice, but not splenomegaly and did not exhibit an accumulation of myeloid cells in the spleen as seen in Ptpn2−/− (BALB/c) mice. Moreover, thymic atrophy, another feature of Ptpn2−/− (BALB/c) mice, was delayed in Ptpn2ex2−/ex2− mice and preceded by an increase in thymocyte positive selection and a concomitant increase in lymph node T cells. Backcrossing Ptpn2−/− (BALB/c) mice onto the C57BL/6 background largely recapitulated the phenotype of Ptpn2ex2−/ex2− mice. Taken together these results reaffirm TCPTP's important role in lymphocyte development and indicate that the effects on morbidity, mortality, bone development and the myeloid compartment are strain-dependent

Mathematical Modelling as a Proof of Concept for MPNs as a Human Inflammation Model for Cancer Development

<p><b>Left:</b> Typical development in stem cells (top panel A) and mature cells (bottom panel B). Healthy hematopoietic cells (full blue curves) dominate in the early phase where the number of malignant cells (stipulated red curves) are few. The total number of cells is also shown (dotted green curves). When a stem cell mutates without repairing mechanisms, a slowly increasing exponential growth starts. At a certain stage, the malignant cells become dominant, and the healthy hematopoietic cells begin to show a visible decline. Finally, the composition between the cell types results in a takeover by the malignant cells, leading to an exponential decline in hematopoietic cells and ultimately their extinction. The development is driven by an approximately exponential increase in the MPN stem cells, and the development is closely followed by the mature MPN cells. <b>Right:</b> B)The corresponding allele burden (7%, 33% and 67% corresponding to ET, PV, and PMF, respectively) defined as the ratio of MPN mature cells to the total number of mature cells.</p

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research