Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition

Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019. Contains supplementary material. arXiv admin note: text overlap with arXiv:1804.0383

Eliciting the Functional Taxonomy from protein annotations and taxa

The advances of omics technologies have triggered the production of an enormous volume of data coming from thousands of species. Meanwhile, joint international efforts like the Gene Ontology (GO) consortium have worked to provide functional information for a vast amount of proteins. With these data available, we have developed FunTaxIS, a tool that is the first attempt to infer functional taxonomy (i.e. how functions are distributed over taxa) combining functional and taxonomic information. FunTaxIS is able to define a taxon specific functional space by exploiting annotation frequencies in order to establish if a function can or cannot be used to annotate a certain species. The tool generates constraints between GO terms and taxa and then propagates these relations over the taxonomic tree and the GO graph. Since these constraints nearly cover the whole taxonomy, it is possible to obtain the mapping of a function over the taxonomy. FunTaxIS can be used to make functional comparative analyses among taxa, to detect improper associations between taxa and functions, and to discover how functional knowledge is either distributed or missing. A benchmark test set based on six different model species has been devised to get useful insights on the generated taxonomic rules

Incorporating molecular data in fungal systematics: a guide for aspiring researchers

The last twenty years have witnessed molecular data emerge as a primary research instrument in most branches of mycology. Fungal systematics, taxonomy, and ecology have all seen tremendous progress and have undergone rapid, far-reaching changes as disciplines in the wake of continual improvement in DNA sequencing technology. A taxonomic study that draws from molecular data involves a long series of steps, ranging from taxon sampling through the various laboratory procedures and data analysis to the publication process. All steps are important and influence the results and the way they are perceived by the scientific community. The present paper provides a reflective overview of all major steps in such a project with the purpose to assist research students about to begin their first study using DNA-based methods. We also take the opportunity to discuss the role of taxonomy in biology and the life sciences in general in the light of molecular data. While the best way to learn molecular methods is to work side by side with someone experienced, we hope that the present paper will serve to lower the learning threshold for the reader.Comment: Submitted to Current Research in Environmental and Applied Mycology - comments most welcom

Evidence-based indications for the planning of PET or PET/CT capacities are needed

Purpose To identify evidence-based indications for PET/PET–CT scans in support of facilities planning and to describe a pilot project in which this information was applied for an investment decision in an Austrian region. The study updates a Health Technology Assessment (HTA) report (2015) on oncological indications, extending it to neurological indications and inflammatory disorders. Methods A systematic literature search to identify HTA reports, evidence-based guidelines, and systematic reviews/meta-analyses (SR/MA) was performed, supplemented by a manual search for professional society recommendations and explicit “not-to-do’s”. A needs-assessment was conducted in the context of the pilot study on investing in an additional PET–CT scanner in the Austrian region of Carinthia. Results Overall recommendations for indications as well as non-recommendations for the three areas (oncology, neurology, and inflammatory disorders) were compiled from the 2015 PET–HTA report and expanded for a final total of ten HTA, comprising 234 (positive and negative) recommendations from professional societies and databases, and supplemented by findings from 23 SR/MA. For the investment decision pilot study in Carinthia, 1762 PET scans were analyzed; 77.8% were assigned to the category “recommended evidence-based indications” (54.7%), “not recommended” (1.8%) or “contradictory recommendations” (21.3%). The remaining could not be assigned to any of the three categories. Conclusions The piloting of PET capacity planning using evidence-based information is a first of its kind in the published literature. On one hand, the high number of PET scans that could not be ascribed to any of the categories identified limits to the instructive power of the study to use evidence-based indication lists as the basis for a needs-assessment investment planning. On the other hand, this study reveals how there is a need to improve indication coding for enhanced capacity planning of medical services. Overall recommendations identified can serve as needs-based and evidence-based decision support for PET/PET–CT service provision

The future of MarBEF’s data legacy

After five years of MarBEF, Europe is now in a position to take the lead in marine biodiversity research. The MarBEF community has built the world’s largest databases on macrobenthos, meiobenthos and pelagic marine species. Well over 100 scientists from 54 institutions in 17 countries have contributed not less than 223 datasets to the MarBEF data system. This has resulted in 4.3 million distribution records of 17,000 species in all the European seas and many of theworld’s oceans. The oldest record dates back to 1768