Luc Mottin

TL;DR: The authors applied machine learning to arsenic species and metallomics profiles of toenails to evaluate associations of environmental arsenic with incident cancer cases, user satisfaction with an AI system for chest X-ray analysis implemented in a hospital’s emergency setting; scaling AI projects for radiology causes and consequences; ECG classification using combination of linear and non-linear features with neural network;dataset comparison tool: utility and privacy; when context matters for credible measurement of drug-drug interactions based on real-world data; a lightweight and interpretable model to classify bundle branch blocks from ECG signals; analysis of stroke assistance in Covid-19 pandemic by process mining techniques; automatic diagnosis of autism spectrum disorder condition using shape based features extracted from brainstem; using explainable supervised machine learning, and an image based object recognition system for wound detection and classification of diabetic foot and venous leg ulcers.

TL;DR: This work investigates a novel ranking pipeline to improve the search of datasets used in biomedical experiments using a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorization method that boosts the rank of datasets matching query constraints.

TL;DR: The Swiss Institute of Bioinformatics Literature Services (SIBiLS) provide personalized Information Retrieval in the biological literature, and allow fully customizable search in semantically enriched contents, based on keywords and/or mapped biomedical entities from a growing set of standardized and legacy vocabularies.

TL;DR: A large query generation module was developed to recognize standard nomenclature as described by the Human Genome Variation Society as well as non-standard formats frequently found in the literature, and a set of different queries with decreasing levels of specificity were designed for the retrieval of scientific abstracts.