Protein-protein docking based on shape complementarity and Voronoi fingerprint

National audiencePredicting the three-dimensional structures of protein-protein complexes is a major challenge for computational biology. Using a Voronoi tessellation model of protein structure, we showed previously that it was possible to use an evolutionary algorithm to train a scoring function to distinguish reliably between native and non-native docking conformations. Here, we show that this approach can be further improved by combining it with rigid body docking predictions generated by the Hex docking algorithm. This new approach is able to rank an acceptable or better conformation within the top 10 predictions for 7 out of the 9 targets available from rounds 8 to 18 of the CAPRI docking experiment.La prédiction de la structure tri-dimensionnelle des complexes protéine-protéine est un enjeu majeur pour la bioinformatique. Nous avions montré dans des travaux précédents que grâce à la modélisation par un diagramme de Voronoï de la structure des protéines, et à l'utilisation d'algorithmes génétiques, il était possible d'optimiser des fonctions de score permettant de distinguer avec une bonne fiabilité les conformations natives des conformations non-natives. Nous montrons dans cet article que cette approche peut être sensiblement améliorée en combinant celle-ci avec des modèles en corps rigide générés par l'algorithme de docking Hex. Cette nouvelle approche, testée sur les cibles CAPRI des rounds 8 à 18, permet de classer dans les 10 meilleures, une conformation quasi-native pour 7 cibles sur les 9 disponibles

La confiance est dans l'air ! Application à l'identification des parcours hospitaliers

National audienceL'extraction de motifs séquentiels permet d'identifier les séquences fréquentes d'événements ordonnés. Afin de résoudre le problème du grand nombre de motifs obtenus, nous proposons l'extension pour les motifs séquentiels de la confiance, mesure d'intérêt utilisée classiquement pour sélectionner les règles d'association. Dans cet article, après avoir présenté les données, nous définirons formellement la notion de confiance appliquée aux motifs séquentiels. Nous appliquerons cette mesure pour identifier des trajectoires hospitalières, représentées par les motifs séquentiels, dans des données issues du PMSI (Programme de Médicalisation des Systèmes d'Information). Nous nous sommes focalisés sur un cas d'étude hospitalière : l'infarctus du myocarde (IM), et notamment la prédiction de la trajectoire des patients ayant eu un IM entre 2009 et 2013. Les résultats obtenus ont été soumis à un spécialiste pour discussion et validation

PaFloChar: An Innovating Approach to Characterise Patient Flows in Myocardial Infarction

The conference will be co-located with VITALIS, the largest eHealth event in Scandinavia. MIE2018 is the 29th Medical Informatics Europe conference and marks the 40th anniversary of the MIE conferences, since the first conference in Cambridge UK. We aim to enable close interaction and networking between an international audience of academics, health professionals, patients and industry partners.International audienceA better knowledge of patient flows would improve decision making in health planning. In this article, we propose a method to characterise patients flows and also to highlight profiles of care pathways considering times and costs. From medico-administrative data, we extracted spatio-temporal patterns. Then, we clustered time between hospitalisations and cost trajectories in order to identify profiles of change over time. This approach may support renewed management strategies

Concept drift vs suicide: How one can help prevent the other?

International audienceSuicide has long been a troublesome problem for society and is an event that has far-reaching consequences. Health organizations such as the World Health Organization (WHO) and the French National Observatory of Suicide (ONS) have pledged to reduce the number of suicides by 10% in all countries by 2020. While suicide is a very marked event, there are often behaviours and words that can act as early signs of predisposition to suicide. The objective of this application is to develop a system that semi-automatically detects these markers through social networks. A previous work has proposed the classification of Tweets using vocabulary in topics related to suicide: sadness, psychological injuries, mental state, depression, fear, loneliness, proposed suicide method, anorexia, insults , and cyber bullying. During that training period, we added a new dimension, time to reflect changes in the status of monitored people. We implemented it with different learning methods including an original concept drift method. We have successfully used this method on synthetic and real data sets issued from the Facebook platform

Breast cancer and quality of life: medical information extraction from health forums

International audienceInternet health forums are a rich textual resource with content generated through free exchanges among patients and, in certain cases, health professionals. We tackle the problem of retrieving clinically relevant information from such forums, with relevant topics being defined from clinical auto-questionnaires. Texts in forums are largely unstructured and noisy, calling for adapted preprocessing and query methods. We minimize the number of false negatives in queries by using a synonym tool to achieve query expansion of initial topic keywords. To avoid false positives, we propose a new measure based on a statistical comparison of frequent co-occurrences in a large reference corpus (Web) to keep only relevant expansions. Our work is motivated by a study of breast cancer patients' health-related quality of life (QoL). We consider topics defined from a breast-cancer specific QoL-questionnaire. We quantify and structure occurrences in posts of a specialized French forum and outline important future developments

Unraveling the molecular architecture of a G protein-coupled receptor/β-arrestin/Erk module complex

International audienceβ-arrestins serve as signaling scaffolds downstream of G protein-coupled receptors, and thus play a crucial role in a plethora of cellular processes. Although it is largely accepted that the ability of β-arrestins to interact simultaneously with many protein partners is key in G protein-independent signaling of GPCRs, only the precise knowledge of these multimeric arrangements will allow a full understanding of the dynamics of these interactions and their functional consequences. However, current experimental procedures for the determination of the three-dimensional structures of protein-protein complexes are not well adapted to analyze these short-lived, multi-component assemblies. We propose a model of the receptor/β-arrestin/Erk1 signaling module, which is consistent with most of the available experimental data. Moreover, for the β-arrestin/Raf1 and the β-arrestin/ERK interactions, we have used the model to design interfering peptides and shown that they compete with both partners, hereby demonstrating the validity of the predicted interaction regions

Finding Relevant Sequences With The Least Temporal Contradiction Measure: Application to Hydrological Data

International audienceIn this paper, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we apply an algorithm to extract sequential patterns on data collected at stations located along several rivers. The data is pre-processed in order to obtain different spatial proximities and the number of patterns is estimated to highlight the influence of defined spatial relationship. We provide an objective measure of assessment, called the least temporal contradiction, to help the expert in discovering new knowledge. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data

Discovering NDM-1 inhibitors using molecular substructure embeddings representations

NDM-1 (New-Delhi-Metallo-beta-lactamase-1) is an enzyme developed by bacteria that is implicated in bacteria resistance to almost all known antibiotics. In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison to the classical Machine Learning paradigm. Moreover, we investigate different compact molecular representations, based on atomic or bi-atomic substructures. Finally, we scanned the Drugbank for strongly active compounds and we present the top-15 ranked compounds