Formal Concept Analysis for the Interpretation of Relational Learning applied on 3D Protein-Binding Sites

International audienceInductive Logic Programming (ILP) is a powerful learning method which allows an expressive representation of the data and produces explicit knowledge. However, ILP systems suffer from a major drawback as they return a single theory based on heuristic user-choices of various parameters, thus ignoring potentially relevant rules. Accordingly, we propose an original approach based on Formal Concept Analysis for effective interpretation of reached theories with the possibility of adding domain knowledge. Our approach is applied to the characterization of three-dimensional (3D) protein-binding sites which are the protein portions on which interactions with other proteins take place. In this context, we define a relational and logical representation of 3D patches and formalize the problem as a concept learning problem using ILP. We report here the results we obtained on a particular category of protein-binding sites namely phosphorylation sites using ILP followed by FCA-based interpretation

Integrative relational machine-learning for understanding drug side-effect profiles

International audienceBackgroundDrug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence.ResultsIn this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site.ConclusionsSide effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs

Éditorial. Leur transition et la nôtre. Crises et justice écologiques

International audienc

Introduction to the project VAHINE: VAriability of vertical and tropHIc transfer of diazotroph derived N in the south wEst Pacific

On the global scale, N₂ fixation provides the major external source of reactive nitrogen to the surface ocean, surpassing atmospheric and riverine inputs, and sustains  ∼  50 % of new primary production in oligotrophic environments. The main goal of the VAriability of vertical and tropHIc transfer of diazotroph derived N in the south wEst Pacific (VAHINE) project was to study the fate of nitrogen newly fixed by diazotrophs (or diazotroph-derived nitrogen) in oceanic food webs, and how it impacts heterotrophic bacteria, phytoplankton and zooplankton dynamics, stocks and fluxes of biogenic elements and particle export. Three large-volume ( ∼  50 m³) mesocosms were deployed in a tropical oligotrophic ecosystem (the New Caledonia lagoon, south-eastern Pacific) and intentionally fertilized with  ∼  0.8 µM of dissolved inorganic phosphorus (DIP) to stimulate diazotrophy and follow subsequent ecosystem changes. VAHINE was a multidisciplinary project involving close collaborations between biogeochemists, molecular ecologist, chemists, marine opticians and modellers. This introductory paper describes in detail the scientific objectives of the project as well as the implementation plan: the mesocosm description and deployment, the selection of the study site (New Caledonian lagoon), and the logistical and sampling strategy. The main hydrological and biogeochemical conditions of the study site before the mesocosm deployment and during the experiment itself are described, and a general overview of the papers published in this special issue is presented

National observation infrastructures in a European framework: COAST-HF A fixed-platform network along French coasts

COAST-HF (Coastal OceAn observing SysTem – High Frequency) is a French national observation network of the physical and biogeochemical dynamics of the coastal ocean, at high frequency. COAST-HF aims at understanding and analysing changes of contrasted coastal ecosystems at different temporal scales from extreme or intermittent high frequency (hour, day) events to multi-year trends. Since several years (from 2000 for the longest time series in Bay of Brest), the network extends along the English Channel, Atlantic and Mediterranean French coasts through 14 fixed platforms instrumented for the in situ high-frequency (≤ 1h) observations. Several French research institutes (IFREMER, CNRS, Marine Universities) are operating these systems. The organization of continuous multi-site in situ observations in a unique network of coastal observing platforms aims at operating an optimal system to pool efforts and initiatives (e.g. human resources for data management), to converge on best practices, and to support common measurement standards. On this basis, key scientific questions can be addressed such as eutrophication processes and effects on dissolved oxygen concentration and phytoplankton dynamics (i.e. in vivo fluorescence), or the influence of main river plumes on sediment dynamics. This coastal in situ observing network contributes for sustained high frequency and long-term observations in coastal environment based on Essential Ocean Variables. Ongoing technological and methodological developments are investigating the continuous observation of chemical (e.g. pCO2, pH) and biological features (e.g. phytoplankton diversity, primary production) that are being implemented in some of these platforms. COAST-HF is part of a national infrastructure (ILICO) and of the European Research Infrastructure project JERICO-NEXT. All those observations are connected to national, regional and European observatory networks and initiatives as the European Ocean Observing System