Search

Your search keyword '"Snowball Inria Associate Team"' showing total 4 results
Start Over Author "Snowball Inria Associate Team" Topic 0302 clinical medicine
4 results on '"Snowball Inria Associate Team"'

1. Knowledge-Based Matching of $n$-ary Tuples

Author

Adrien Coulet, Pierre Monnin, Miguel Couceiro, Amedeo Napoli, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Supported by the PractiKPharma project, founded by the French National Research Agency (ANR) under Grant ANR15-CE23-0028, by the IDEX 'Lorraine Université d’Excellence' (15-IDEX-0004), and by the Snowball Inria Associate Team., Mehwish Alam, Tanya Braun, Bruno Yun, Snowball Inria Associate Team, ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), ANR-15-IDEX-0004,LUE,Isite LUE(2015), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Matching (statistics), Computer Science - Artificial Intelligence, Computer science, Ontology (information science), Domain (software engineering), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Matching, Semantic Web, 030304 developmental biology, Alignment, 0303 health sciences, Information retrieval, business.industry, Ontology, Artificial Intelligence (cs.AI), Knowledge base, n-ary Tuple, Software agent, 030220 oncology & carcinogenesis, Domain knowledge, Order, Tuple, business
Abstract

International audience; An increasing number of data and knowledge sources are accessible by human and software agents in the expanding Semantic Web. Sources may differ in granularity or completeness, and thus be complementary. Consequently, they should be reconciled in order to unlock the full potential of their conjoint knowledge. In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar. This task is challenging since knowledge units can be heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we focus on matching $n$-ary tuples in a knowledge base with a rule-based methodology. To alleviate heterogeneity issues, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples from four different real-world sources. Results highlight noteworthy agreements and particularities within and across sources.

Published
2020
Full Text
View/download PDF

2. PGxCorpus, a manually annotated corpus for pharmacogenomics

Author

Kevin Dalleau, Joël Legrand, Adrien Coulet, Nadine Petitpain, Malika Smaïl-Tabbone, Romain Gogdemir, Yannick Toussaint, William Digan, Cédric Bousquet, Marie-Dominique Devignes, Patrice Ringot, Ndeye-Coumba Ndiaye, Chia-Ju Lee, Natural Language Processing : representations, inference and semantics (SYNALP), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Computational Algorithms for Protein Structures and Interactions (CAPSID), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Department of Biomedical Informatics and Medical Education, University of Washington, University of Washington [Seattle], Nutrition-Génétique et Exposition aux Risques Environnementaux (NGERE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre Régional de PharmacoVigilance de Lorraine (CRPV Lorraine), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), ANR-15-CE23-0028, Agence Nationale de la Recherche, 15-IDEX-0004, Université de Lorraine, Snowball Inria Associate Team, GRID5000, ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), Coulet, Adrien, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique - - PractiKPharma2015 - ANR-15-CE23-0028 - AAPG2015 - VALID, CRHU Nancy, and Service Informatique de Soutien à la Recherche (SISR)

Subjects
Data Descriptor, Computer science, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, Resource (project management), Drug response, 030212 general & internal medicine, lcsh:Science, Data Curation, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 0303 health sciences, 3. Good health, Computer Science Applications, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Supervised Machine Learning, Statistics, Probability and Uncertainty, Natural language processing, Information Systems, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Statistics and Probability, PubMed, [SDV.SP.MED] Life Sciences [q-bio]/Pharmaceutical sciences/Medication, [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, Library and Information Sciences, Domain (software engineering), Education, 03 medical and health sciences, [SDV.SP.MED]Life Sciences [q-bio]/Pharmaceutical sciences/Medication, Component (UML), Genetics, Humans, 030304 developmental biology, business.industry, Health care, Significant part, Precision medicine, ComputingMethodologies_PATTERNRECOGNITION, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Pharmacogenetics, Pharmacogenomics, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, lcsh:Q, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer
Abstract

Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes PGx-related knowledge a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly reusable by humans or software. Natural language processing techniques have been developed to guide experts who curate this amount of knowledge. But existing works are limited by the absence of a high quality annotated corpus focusing on PGx domain. In particular, this absence restricts the use of supervised machine learning. This article introduces PGxCorpus, a manually annotated corpus, designed to fill this gap and to enable the automatic extraction of PGx relationships from text. It comprises 945 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly gene variations, genes, drugs and phenotypes), and relationships between those. In this article, we present the corpus itself, its construction and a baseline experiment that illustrates how it may be leveraged to synthesize and summarize PGx knowledge., Measurement(s)gene_variant • response to drug • textual entity • chemical entity • haplotype • gene • Pharmacogenomics • Pharmacogenetics • abbreviation textual entity • Pharmacokinetics • Pharmacodynamics • phenotypeTechnology Type(s)digital curationSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11323724

Published
2020
Full Text
View/download PDF

3. Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk

Author

Fatima Rodriguez, Ben J. Marafino, Nigam H. Shah, Latha Palaniappan, Stephen R. Pfohl, Adrien Coulet, Stanford Center for BioMedical Informatics Research (BMIR), Stanford University, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, and Snowball Inria Associate Team

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Actuarial science, Atherosclerotic cardiovascular disease, Computer science, Machine Learning (stat.ML), 030204 cardiovascular system & hematology, Health records, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Statistics - Machine Learning, Cohort, Risk stratification, Leverage (statistics), Observational study, 030212 general & internal medicine, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups withinconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficial therapy. In this work, we leverage adversarial learning and a large observational cohort extracted from electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reducedvariability in error rates across groups. We empirically demonstrate that our approach is capable of aligning the distribution of risk predictions conditioned on the outcome across several groups simultaneously for models built from high-dimensional EHR data. We also discuss the relevance ofthese results in the context of the empirical trade-off between fairness and model performance.

Published
2019
Full Text
View/download PDF

4. Learning from biomedical linked data to suggest valid pharmacogenes

Author

Adrien Coulet, Kevin Dalleau, Patrice Ringot, Sébastien Da Silva, Ndeye Coumba Ndiaye, Yassine Marzougui, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Ecole Nationale Supérieure des Mines de Nancy (ENSMN), Institut Mines-Télécom [Paris] (IMT)-Université de Lorraine (UL), Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), ANR PractiKPharma project, grant ANR-15-CE23-0028, funded by the French National Research Agency (http://practikpharma.loria.fr/) and *Snowflake, an Inria associate team (http://snowflake.loria.fr/), Snowflake Inria Associate Team, Inria@SiliconValley, Snowball Inria Associate Team, ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Coulet, Adrien, and Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique - - PractiKPharma2015 - ANR-15-CE23-0028 - AAPG2015 - VALID

Subjects
0301 basic medicine, Graph kernel, Computer science, Knowledge discovery from databases, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, False positive paradox, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Valid pharmacogenes, Linked data, Computer Science Applications, Random forest, Identification (information), Phenotype, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, 030220 oncology & carcinogenesis, lcsh:R858-859.7, Information Systems, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Networks and Communications, Health Informatics, [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, lcsh:Computer applications to medicine. Medical informatics, Machine learning, Set (abstract data type), 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Computer Graphics, Selection (linguistics), Data mining, Semantic Web, Linkage (software), [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Research, Computational Biology, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], 030104 developmental biology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Pharmacogenetics, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Pharmacogenomics, computer
Abstract

Background A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available. Method We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene–drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods –random forest and graph kernel–, which results are compared in this article. Results We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed. Electronic supplementary material The online version of this article (doi:10.1186/s13326-017-0125-1) contains supplementary material, which is available to authorized users.

Published
2017
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results