Active Learning Stopping Strategies for Technology-Assisted Sensitivity Review

Active learning strategies are often deployed in technology-assisted review tasks, such as e-discovery and sensitivity review, to learn a classifier that can assist the reviewers with their task. In particular, an active learning strategy selects the documents that are expected to be the most useful for learning an effective classifier, so that these documents can be reviewed before the less useful ones. However, when reviewing for sensitivity, the order in which the documents are reviewed can impact on the reviewers' ability to perform the review. Therefore, when deploying active learning in technology-assisted sensitivity review, we want to know when a sufficiently effective classifier has been learned, such that the active learning can stop and the reviewing order of the documents can be selected by the reviewer instead of the classifier. In this work, we propose two active learning stopping strategies for technology-assisted sensitivity review. We evaluate the effectiveness of our proposed approaches in comparison with three state-of-the-art stopping strategies from the literature. We show that our best performing approach results in a significantly more effective sensitivity classifier (+6.6% F2) than the best performing stopping strategy from the literature (McNemar's test, p<0.05)

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences

Background: Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. Methods: We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. Results: By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Conclusions: Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.X116sciescopu

Quantitative modeling of the physiology of ascites in portal hypertension

Although the factors involved in cirrhotic ascites have been studied for a century, a number of observations are not understood, including the action of diuretics in the treatment of ascites and the ability of the plasma-ascitic albumin gradient to diagnose portal hypertension. This communication presents an explanation of ascites based solely on pathophysiological alterations within the peritoneal cavity. A quantitative model is described based on experimental vascular and intraperitoneal pressures, lymph flow, and peritoneal space compliance. The model's predictions accurately mimic clinical observations in ascites, including the magnitude and time course of changes observed following paracentesis or diuretic therapy

AN INTEGRATED APPROACH TO EVALUATE AND MONITOR THE CONSERVATION STATE OF CORALLIGENOUS BOTTOMS: THE INDEX-COR METHOD

International audienceCoralligenous outcrops represent a "hotspot" of Mediterranean marine biodiversity. Algae and sessile invertebrate taxa (mainly sponges, cnidarians, bryozoans and tunicates) structure the associated benthic assemblages and constitute remarkable seascapes. Nevertheless, this fragile habitat is submitted to a wide array of human impacts such as sewage outfalls, eutrophication, physical impacts linked to fishing and diving activities, as well as global warming effects. The current European legislative context (EU WFD, EU Habitat Directive, EU MSFD) imposes to reach or maintain a good environmental status for marine ecosystems. In this context, the MPA stakeholders need to have robust and accessible tools allowing the evaluation of the conservation state of the habitats. Concerning coralligenous bottoms, we propose a new method based on an integrated approach taking into account (i) the ratio between sensitive and tolerant species according to human impacts, (ii) the richness of macrotaxonomic descriptors assessed from direct observation (in situ or from images) and (iii) their structural complexity (basal, intermediate and upper layers present in coralligenous bottoms). These three metrics are combined into a global index called INDEX-COR. Datasets were acquired along the French coasts. In each site, 2 transects 15m long were installed on the bottom. Along each transect, 15 photo quadrats (40 cm x 60 cm) and 1 video were recorded and notes were taken by a SCUBA diver-Observer. This method was applied between 15 and 50 meters depth and can be also performed by a ROV (Remotely Operating Vehicule) or an AUV (Autonomous Underwater Vehicle). INDEX-COR is intended to be applied to other Mediterranean areas using metrics and species lists adapted to the different regional contexts