Decision Diagrams for Qualitative Biological Models

The modelisation of large biological systems such that metabolic networks or gene interaction networks, implies interpretations of heterogeneous biological knownledge. A common feature of observations accumulated by biologist on these systems is their qualitative nature. More over it often happens that the knownledge is based on comparisons between different experimental conditions. In previous papers variations in observed variables between two conditions was interpreted in terms of equilibria shift. This leads to use qualitative equations in a sign algebra. The present report gives details on the implementation and main algorithms involved

Neural Networks beyond explainability: Selective inference for sequence motifs

Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of deep learning, tools have been devised to extract features such as sequence motifs that can explain the predictions made by a trained network. Here we intend to go beyond explainable machine learning and introduce SEISM, a selective inference procedure to test the association between these extracted features and the predicted phenotype. In particular, we discuss how training a one-layer convolutional network is formally equivalent to selecting motifs maximizing some association score. We adapt existing sampling-based selective inference procedures by quantizing this selection over an infinite set to a large but finite grid. Finally, we show that sampling under a specific choice of parameters is sufficient to characterize the composite null hypothesis typically used for selective inference-a result that goes well beyond our particular framework. We illustrate the behavior of our method in terms of calibration, power and speed and discuss its power/speed trade-off with a simpler data-split strategy. SEISM paves the way to an easier analysis of neural networks used in regulatory genomics, and to more powerful methods for genome wide association studies (GWAS)

BioNLP Shared Task 2011 - Bacteria Gene Interactions and Renaming

Document Type : Proceedings Paper Conference Date : JUN 23-24, 2011 Conference Location : Portland, ORInternational audienceWe present two related tasks of the BioNLP Shared Tasks 2011: Bacteria Gene Renaming (Rename) and Bacteria Gene Interactions (GI). We detail the objectives, the corpus specification, the evaluation metrics, and we summarize the participants' results. Both issued from PubMed scientific literature abstracts, the Rename task aims at extracting gene name synonyms, and the GI task aims at extracting genic interaction events, mainly about gene transcriptional regulations in bacteria

Path-equivalent developments in acyclic weighted automata

International audienceWeighted finite automata (WFA) are used with FPGA accelerating hardware to scan large genomic banks. Hardwiring such automata raises surface area and clock frequency constraints, requiring efficient ε-transitions-removal techniques. In this paper, we present bounds on the number of new transitions for the development of acyclic WFA, which is a special case of the ε-transitions-removal problem. We introduce a new problem, a partial removal of ε-transitions while accepting short chains of ε-transitions

BioNLP Shared Task - The Bacteria Track

Background: We present the BioNLP 2011 Shared Task Bacteria Track, the first Information Extraction challenge entirely dedicated to bacteria. It includes three tasks that cover different levels of biological knowledge. The Bacteria Gene Renaming supporting task is aimed at extracting gene renaming and gene name synonymy in PubMed abstracts. The Bacteria Gene Interaction is a gene/protein interaction extraction task from individual sentences. The interactions have been categorized into ten different sub-types, thus giving a detailed account of genetic regulations at the molecular level. Finally, the Bacteria Biotopes task focuses on the localization and environment of bacteria mentioned in textbook articles. We describe the process of creation for the three corpora, including document acquisition and manual annotation, as well as the metrics used to evaluate the participants' submissions. Results: Three teams submitted to the Bacteria Gene Renaming task; the best team achieved an F-score of 87%. For the Bacteria Gene Interaction task, the only participant's score had reached a global F-score of 77%, although the system efficiency varies significantly from one sub-type to another. Three teams submitted to the Bacteria Biotopes task with very different approaches; the best team achieved an F-score of 45%. However, the detailed study of the participating systems efficiency reveals the strengths and weaknesses of each participating system. Conclusions: The three tasks of the Bacteria Track offer participants a chance to address a wide range of issues in Information Extraction, including entity recognition, semantic typing and coreference resolution. We found commond trends in the most efficient systems: the systematic use of syntactic dependencies and machine learning. Nevertheless, the originality of the Bacteria Biotopes task encouraged the use of interesting novel methods and techniques, such as term compositionality, scopes wider than the sentence

Strategies of initiation and streamlining of antibiotic therapy in 41 French intensive care units

CIAR (Club d'infectiologie en Anesthésie-Réanimation) Study Group: Pr B Allaouchiche (HCL, CHU Lyon), Pr C Arich (CHU Nimes), Pr C Auboyer (CHU St-Etienne), Dr JP Caramella (CHG Nevers), Dr JF Cochard (CHU Bordeaux), Dr A Combes (CHG Meaux), Dr P Courant (CHG Avignon), Dr J Durand-Gasselin (CHG Toulon), Pr J Duranteau (APHP, CHU Bicetre), Dr H Floch (CHU Nantes), Dr F Fraisse (CHG St Denis), Pr M Freysz (CHU Dijon), Dr B Garrigues (CHG Aix-en-Provence), Dr B Georges (CHU Toulouse), Pr F Gouin (APHM, CHU Marseille), Pr L Jacob (APHP, CHU St Louis), Pr P Juvin (APHP, CHU Beaujon), Dr J Keinlen (CHU Montpellier), Dr AM Korinek (APHP, CHU Pitie Salpetriere), Dr C Lamer (Institut Mutualiste Montsouris, Paris), Pr JY Lefrant (CHU Nimes), Dr O Lesieur (CHG La Rochelle), Dr Yazine Mahjoub (CHU Amiens), Pr Y Malledant (CHU Rennes), Pr C Martin (APHM, CHU Marseille), Pr O Mimoz (CHU Poitiers), Pr C Paugam-Burtz (APHP, CHU Beaujon, Clichy), Dr PF Perrigault (CHU Montpellier), Pr T Pottecher (CHU Strasbourg), Pr JL Pourriat (APHP, CHU Hotel Dieu), Dr JF Poussel (CHG Metz), Dr A Rabbat (APHP, CHU Hotel Dieu), Dr J Reignier (CHG La Roche sur Yon), Dr P Sichel (CHG Cherbourg), Dr JP Sollet (CHG Argenteuil), Dr D Thevenin (CHG Lens), Dr G Viquesnel (CHU Caen).International audienceINTRODUCTION: Few studies have addressed the decision-making process of antibiotic therapy (AT) in intensive care unit (ICU) patients. METHODS: In a prospective observational study, all consecutive patients admitted over a one-month period (2004) to 41 French surgical (n = 22) or medical/medico-surgical ICUs (n = 19) in 29 teaching university and 12 non-teaching hospitals were screened daily for AT until ICU discharge. We assessed the modalities of initiating AT, reasons for changes and factors associated with in ICU mortality including a specific analysis of a new AT administered on suspicion of a new infection. RESULTS: A total of 1,043 patients (61% of the cohort) received antibiotics during their ICU stay. Thirty percent (509) of them received new AT mostly for suspected diagnosis of pneumonia (47%), bacteremia (24%), or intra-abdominal (21%) infections. New AT was prescribed on day shifts (45%) and out-of-hours (55%), mainly by a single senior physician (78%) or by a team decision (17%). This new AT was mainly started at the time of suspicion of infection (71%) and on the results of Gram-stained direct examination (21%). Susceptibility testing was performed in 261 (51%) patients with a new AT. This new AT was judged inappropriate in 58 of these 261 (22%) patients. In ICUs with written protocols for empiric AT (n = 25), new AT prescribed before the availability of culture results (P = 0.003) and out-of-hours (P = 0.04) was more frequently observed than in ICUs without protocols but the appropriateness of AT was not different. In multivariate analysis, the predictive factors of mortality for patients with new AT were absence of protocols for empiric AT (adjusted odds ratio (OR) = 1.64, 95% confidence interval (95%CI): 1.01 to 2.69), age ≥60 (OR = 1.97, 95% CI: 1.19 to 3.26), SAPS II score >38 (OR = 2.78, 95% CI: 1.60 to 4.84), rapidly fatal underlying diseases (OR = 2.91, 95% CI: 1.52 to 5.56), SOFA score ≥6 (OR = 4.48, 95% CI: 2.46 to 8.18). CONCLUSIONS: More than 60% of patients received AT during their ICU stay. Half of them received new AT, frequently initiated out-of-hours. In ICUs with written protocols, empiric AT was initiated more rapidly at the time of suspicion of infection and out-of-hours. These results encourage the establishment of local recommendations for empiric AT