Bridging the demand and the offer in data science

During the last several years, we have observed an exponential increase in the demand for Data Scientists in the job market. As a result, a number of trainings, courses, books, and university educational programs (both at undergraduate, graduate and postgraduate levels) have been labeled as “Big data” or “Data Science”; the fil‐rouge of each of them is the aim at forming people with the right competencies and skills to satisfy the business sector needs. In this paper, we report on some of the exercises done in analyzing current Data Science education offer and matching with the needs of the job markets to propose a scalable matching service, ie, COmpetencies ClassificatiOn (E‐CO‐2), based on Data Science techniques. The E‐CO‐2 service can help to extract relevant information from Data Science–related documents (course descriptions, job Ads, blogs, or papers), which enable the comparison of the demand and offer in the field of Data Science Education and HR management, ultimately helping to establish the profession of Data Scientist.publishedVersio

ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols

United we stand: Using multiple strategies for topic labeling

International audienceTopic labeling aims at providing a sound, possibly multi-words, label that depicts a topic drawn from a topic model. This is of the utmost practical interest in order to quickly grasp a topic informa-tional content-the usual ranked list of words that maximizes a topic presents limitations for this task. In this paper, we introduce three new unsupervised n-gram topic labelers that achieve comparable results than the existing unsupervised topic labelers but following different assumptions. We demonstrate that combining topic labelers-even only two-makes it possible to target a 64% improvement with respect to single topic labeler approaches and therefore opens research in that direction. Finally, we introduce a fourth topic labeler that extracts representative sentences, using Dirichlet smoothing to add contextual information. This sentence-based labeler provides strong surrogate candidates when n-gram topic labelers fall short on providing relevant labels, leading up to 94% topic covering

Urinary peptides in heart failure: a link to molecular pathophysiology

International audienceAims: Heart failure (HF) is a major public health concern worldwide. The diversity of HF makes it challenging to decipher the underlying complex pathological processes using single biomarkers. We examined the association between urinary peptides and HF with reduced (HFrEF), mid-range (HFmrEF) and preserved (HFpEF) ejection fraction, defined based on the European Society of Cardiology guidelines, and the links between these peptide biomarkers and molecular pathophysiology.Methods and results: Analysable data from 5608 participants were available in the Human Urinary Proteome database. The urinary peptide profiles from participants diagnosed with HFrEF, HFmrEF, HFpEF and controls matched for sex, age, estimated glomerular filtration rate, systolic and diastolic blood pressure, diabetes and hypertension were compared applying the Mann-Whitney test, followed by correction for multiple testing. Unsupervised learning algorithms were applied to investigate groups of similar urinary profiles. A total of 577 urinary peptides significantly associated with HF were sequenced, 447 of which (77%) were collagen fragments. In silico analysis suggested that urinary biomarker abnormalities in HF principally reflect changes in collagen turnover and immune response, both associated with fibrosis. Unsupervised clustering separated study participants into two clusters, with 83% of non-HF controls allocated to cluster 1, while 65% of patients with HF were allocated to cluster 2 (P < 0.0001). No separation based on HF subtype was detectable.Conclusions: Heart failure, irrespective of ejection fraction subtype, was associated with differences in abundance of urinary peptides reflecting collagen turnover and inflammation. These peptides should be studied as tools in early detection, prognostication, and prediction of therapeutic response