Integrating human exposome data into next-generation risk assessment: a systematic framework to infer real-life exposure levels and prioritize chemical compounds for testing Yifeng Dai, Bob van de Water, Jaione Telleria, Laura I. Furlong, Roel C.H. Vermeulen, Jelle Vlaanderen Environment International, 2026 • Developed a flexible framework integrating exposome data into NGRA workflows. • Combined exposome, model, toxicity, and bioactivity data for chemical prioritization. • Developed Tox and ExpTox scores to prioritize chemicals. • Exposure data substantially reshaped the chemical ranking. Exposome research and next-generation risk assessment (NGRA) share the goal of improving the human relevance of chemical safety evaluations, yet practical integration remains limited. We developed a flexible framework that combines human biomonitoring (HBM) data, exposome datasets, and exposure modeling results with chemical property, toxicity, and bioactivity information to support NGRA and chemical prioritization. The framework comprises three modules: a curated human exposome database, model-based exposure estimates, and toxicity/bioactivity data integration. Its application was demonstrated in two case studies. Case study 1 focused on organ-specific carcinogens, prioritizing candidate hepatocarcinogens. Case study 2 adopted an exposure-driven approach, highlighting compounds frequently detected in biomonitoring programs across populations. The framework generated quantitative ranges of compound concentrations in biological samples, predicted blood concentrations, and external intake values, which can be compared with in vitro toxicity benchmarks such as half-maximal activity concentration (AC50). Results show that incorporating human exposure data substantially influences chemical prioritization. This framework provides a practical pathway for embedding exposome data into NGRA workflows, thereby strengthening human-relevant risk assessments and informing future chemical safety evaluations.
Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety Christopher E. Goldring, Giusy Russomanno, Carmen Pin, Panuwat Trairatphisan, Kylie A. Beattie, Ciarán P. Fisher, Janet Piñero, Richard J. Brennan, Diana Clausznitzer, Ian M. Copple, Theo M. de Kok, Carrie A. Duckworth, Laura I. Furlong, Barbara Füzi, Attila Gabor, Louis Gall, Jan Hengstler, Henning Hermjakob, Fiona Hunter, Danyel Jennen, Mikko Koskinen, Steven J. Kunnen, Lieve Lammens, Sebastian Lobentanzer, Marcel Mohr, Elisa Passini, D. Mark Pritchard, Rahuman S. Malik-Sheriff, Blanca Rodríguez, Eric I. Rossman, Julio Saez-Rodríguez, Friedemann Schmidt, Rowena Sison-Young, Inari Soininen, Sean Turner, Bob van de Water, Johan G. C. van Hasselt, Filippo Venezia, Jeffrey A. Willy, Derek J. Leishman, James L. Stevens, Loic Laplanche Nature Reviews Drug Discovery, 2026
Identifying time patterns in Huntington’s disease trajectories using dynamic time warping-based clustering on multi-modal data Alexia Giannoula, Audrey E. De Paepe, Ferran Sanz, Laura I. Furlong, Estela Camara Scientific Reports, 2025 One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics. Building on an earlier methodology, a new dimension of time-varying clinical and imaging features is incorporated, through an adapted cost-minimization algorithm for clustering on different, possibly overlapping, feature subsets. The model disease chosen is Huntington's disease (HD), characterized by progressive neurodegeneration. From a wide range of examined user-defined parameters, four case examples are highlighted to demonstrate the identified temporal patterns in multi-modal HD trajectories and to study how these differ due to the combined effects of feature weights and granularity threshold. For each identified cluster, polynomial fits that describe the time behavior of the assessed features are provided for an informative comparison, together with their averaged values. The proposed data-mining methodology permits the stratification of distinct time patterns of multi-modal health data in individuals that share a diagnosis, by employing user-customized criteria beyond the current clinical practice. Overall, this work bears implications for better analysis of individual variability in disease progression, opening doors to personalized preventative, diagnostic and therapeutic strategies.
PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports Javier Corvi, Nicolás Díaz-Roussel, José M. Fernández, Francesco Ronzano, Emilio Centeno, Pablo Accuosto, Celine Ibrahim, Shoji Asakura, Frank Bringezu, Mirjam Fröhlicher, Annika Kreuchwig, Yoko Nogami, Jeong Rih, Raul Rodriguez-Esteban, Nicolas Sajot, Joerg Wichard, Heng-Yi Michael Wu, Philip Drew, Thomas Steger-Hartmann, Alfonso Valencia, Laura I. Furlong, Salvador Capella-Gutierrez Journal of Cheminformatics, 2025 Over the last few decades the pharmaceutical industry has generated a vast corpus of knowledge on the safety and efficacy of drugs. Much of this information is contained in toxicology reports, which summarise the results of animal studies designed to analyse the effects of the tested compound, including unintended pharmacological and toxic effects, known as treatment-related findings. Despite the potential of this knowledge, the fact that most of this relevant information is only available as unstructured text with variable degrees of digitisation has hampered its systematic access, use and exploitation. Text mining technologies have the ability to automatically extract, analyse and aggregate such information, providing valuable new insights into the drug discovery and development process. In the context of the eTRANSAFE project, we present PretoxTM (Preclinical Toxicology Text Mining), the first system specifically designed to detect, extract, organise and visualise treatment-related findings from toxicology reports. The PretoxTM tool comprises three main components: PretoxTM Corpus, PretoxTM Pipeline and PretoxTM Web App. The PretoxTM Corpus is a gold standard corpus of preclinical treatment-related findings annotated by toxicology experts. This corpus was used to develop, train and validate the PretoxTM Pipeline, which extracts treatment-related findings from preclinical study reports. The extracted information is then presented for expert visualisation and validation in the PretoxTM Web App. Scientific Contribution While text mining solutions have been widely used in the clinical domain to identify adverse drug reactions from various sources, no similar systems exist for identifying adverse events in animal models during preclinical testing. PretoxTM fills this gap by efficiently extracting treatment-related findings from preclinical toxicology reports. This provides a valuable resource for toxicology research, enhancing the efficiency of safety evaluations, saving time, and leading to more effective decision-making in the drug development process.
Mapping Longitudinal Psychiatric Signatures in Huntington’s Disease Audrey E De Paepe, Alexia Giannoula, Clara Garcia-Gorro, Nadia Rodriguez-Dechicha, Irene Vaquer, Matilde Calopa, Ferran Sanz, Laura I Furlong, Ruth de Diego-Balaguer, Estela Camara Archives of Clinical Neuropsychology, 2025 Objective Although Huntington’s disease is characterized by motor onset, psychiatric disturbances may present years prior and affect functioning. However, there is inter-individual variability in psychiatric expression and progression. This study therefore strives to stratify longitudinal psychiatric signatures that may inform Huntington’s disease prognosis, with potential clinical applications. Methods Forty-six Huntington’s disease gene carriers (21 premanifest, 25 manifest; 31 female; age range 25–69) underwent short-Problem Behavior Assessment for depression, irritability, apathy, and dysexecutive behaviors for up to six longitudinal visits. The Disease Trajectories software, a machine-learning approach, was employed to perform unsupervised clustering of psychiatric trajectories. Linear fits were calculated for each cluster. Lastly, the main clusters of shared trajectories were assessed for group differences in demographic and clinical characteristics. Results The Disease Trajectories analysis software identified two main psychiatric patterns comprising premanifest and manifest patients that explained 54% of the sample. These two clusters evinced a dissociation in the development of depression and irritability; the first cluster was defined by increasing irritability with no depression and the second by a rise-and-fall in depression with no irritability. Both clusters showed a longitudinal increase in clinically relevant apathy and dysexecutive behaviors. Conclusions Ultimately, through the detection of individual-level psychiatric trajectories with machine-learning, this exploratory study reveals that a dissociation of depression and irritability is apparent even in premanifest stages. These findings underscore individual differences in the severity of longitudinal multivariate clinical characteristics for real-world patient stratification, with implications for precision medicine.
eTRANSAFE: data science to empower translational safety assessment Ferran Sanz, François Pognan, Thomas Steger-Hartmann, Carlos Díaz, Shoji Asakura, Alexander Amberg, Nathalie Bécourt-Lhote, Niklas Blomberg, Nicolas Bosc, Katharine Briggs, Frank Bringezu, Claire Brulle-Wohlhueter, Søren Brunak, Ruud Bueters, Giulia Callegaro, Salvador Capella-Gutierrez, Emilio Centeno, Javier Corvi, Mark T. D. Cronin, Philip Drew, Guillemette Duchateau-Nguyen, Gerhard F. Ecker, Sylvia Escher, Eloy Felix, Miguel Ferreiro, Markus Frericks, Laura I. Furlong, Robert Geiger, Catherine George, Melanie Grandits, Dragomir Ivanov-Draganov, Jean Kilgour-Christie, Tevfik Kiziloren, Jan A. Kors, Naoki Koyama, Annika Kreuchwig, Andrew R. Leach, Miguel-Angel Mayer, Peter Monecke, Wolfgang Muster, Chihiro Miyamoto Nakazawa, Gavin Nicholson, Rowan Parry, Manuel Pastor, Janet Piñero, Nils Oberhauser, Juan Manuel Ramírez-Anguita, Adrián Rodrigo, Aljosa Smajic, Markus Schaefer, Sebastian Schieferdecker, Inari Soininen, Emma Terricabras, Panuwat Trairatphisan, Sean C. Turner, Alfonso Valencia, Bob van de Water, Johan L. van der Lei, Erik M. van Mulligen, Esther Vock, David Wilkinson Nature Reviews Drug Discovery, 2023
Identifying multiscale translational safety biomarkers using a network-based systems approach Giulia Callegaro, Johannes P. Schimming, Janet Piñero González, Steven J. Kunnen, Lukas Wijaya, Panuwat Trairatphisan, Linda van den Berk, Kim Beetsma, Laura I. Furlong, Jeffrey J. Sutherland, Jennifer Mollon, James L. Stevens, Bob van de Water Iscience, 2023 Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4dependent stress response and potential early safety signals.
Assessing network-based methods in the context of system toxicology Jordi Valls-Margarit, Janet Piñero, Barbara Füzi, Natacha Cerisier, Olivier Taboureau, Laura I. Furlong Frontiers in Pharmacology, 2023 Introduction: Network-based methods are promising approaches in systems toxicology because they can be used to predict the effects of drugs and chemicals on health, to elucidate the mode of action of compounds, and to identify biomarkers of toxicity. Over the years, the network biology community has developed a wide range of methods, and users are faced with the task of choosing the most appropriate method for their own application. Furthermore, the advantages and limitations of each method are difficult to determine without a proper standard and comparative evaluation of their performance. This study aims to evaluate different network-based methods that can be used to gain biological insight into the mechanisms of drug toxicity, using valproic acid (VPA)-induced liver steatosis as a benchmark.Methods: We provide a comprehensive analysis of the results produced by each method and highlight the fact that the experimental design (how the method is applied) is relevant in addition to the method specifications. We also contribute with a systematic methodology to analyse the results of the methods individually and in a comparative manner.Results: Our results show that the evaluated tools differ in their performance against the benchmark and in their ability to provide novel insights into the mechanism of adverse effects of the drug. We also suggest that aggregation of the results provided by different methods provides a more confident set of candidate genes and processes to further the knowledge of the drug’s mechanism of action.Discussion: By providing a detailed and systematic analysis of the results of different network-based tools, we aim to assist users in making informed decisions about the most appropriate method for systems toxicology applications.
Genomic and proteomic biomarker landscape in clinical trials Janet Piñero, Pablo S. Rodriguez Fraga, Jordi Valls-Margarit, Francesco Ronzano, Pablo Accuosto, Ricard Lambea Jane, Ferran Sanz, Laura I. Furlong Computational and Structural Biotechnology Journal, 2023 The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.
Visualization of automatically combined disease maps and pathway diagrams for rare diseases Piotr Gawron, David Hoksza, Janet Piñero, Maria Peña-Chilet, Marina Esteban-Medina, Jose Luis Fernandez-Rueda, Vincenza Colonna, Ewa Smula, Laurent Heirendt, François Ancien, Valentin Groues, Venkata P. Satagopam, Reinhard Schneider, Joaquin Dopazo, Laura I. Furlong, Marek Ostaszewski Frontiers in Bioinformatics, 2023
Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches Anna Niarakis, Marek Ostaszewski, Alexander Mazein, Inna Kuperstein, Martina Kutmon, Marc E. Gillespie, Akira Funahashi, Marcio Luis Acencio, Ahmed Hemedan, Michael Aichem, Karsten Klein, Tobias Czauderna, Felicia Burtscher, Takahiro G. Yamada, Yusuke Hiki, Noriko F. Hiroi, Finterly Hu, Nhung Pham, Friederike Ehrhart, Egon L. Willighagen, Alberto Valdeolivas, Aurelien Dugourd, Francesco Messina, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Sylvain Soliman, Sara Sadat Aghamiri, Bhanwar Lal Puniya, Aurélien Naldi, Tomáš Helikar, Vidisha Singh, Marco Fariñas Fernández, Viviam Bermudez, Eirini Tsirvouli, Arnau Montagud, Vincent Noël, Miguel Ponce-de-Leon, Dieter Maier, Angela Bauch, Benjamin M. Gyori, John A. Bachman, Augustin Luna, Janet Piñero, Laura I. Furlong, Irina Balaur, Adrien Rougny, Yohan Jarosz, Rupert W. Overall, Robert Phair, Livia Perfetto, Lisa Matthews, Devasahayam Arokia Balaya Rex, Marija Orlic-Milacic, Luis Cristobal Monraz Gomez, Bertrand De Meulder, Jean Marie Ravel, Bijay Jassal, Venkata Satagopam, Guanming Wu, Martin Golebiewski, Piotr Gawron, Laurence Calzone, Jacques S. Beckmann, Chris T. Evelo, Peter D’Eustachio, Falk Schreiber, Julio Saez-Rodriguez, Joaquin Dopazo, Martin Kuiper, Alfonso Valencia, Olaf Wolkenhauer, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider, and Frontiers in Immunology, 2023
The etransafe project on translational safety assessment through integrative knowledge management: Achievements and perspectives François Pognan, Thomas Steger-Hartmann, Carlos Díaz, Niklas Blomberg, Frank Bringezu, Katharine Briggs, Giulia Callegaro, Salvador Capella-Gutierrez, Emilio Centeno, Javier Corvi, Philip Drew, William C. Drewe, José M. Fernández, Laura I. Furlong, Emre Guney, Jan A. Kors, Miguel Angel Mayer, Manuel Pastor, Janet Piñero, Juan Manuel Ramírez-Anguita, Francesco Ronzano, Philip Rowell, Josep Saüch-Pitarch, Alfonso Valencia, Bob van de Water, Johan van der Lei, Erik van Mulligen, Ferran Sanz Pharmaceuticals, 2021
COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms Marek Ostaszewski, Alexander Mazein, Marc E. Gillespie, Inna Kuperstein, Anna Niarakis, Henning Hermjakob, Alexander R. Pico, Egon L. Willighagen, Chris T. Evelo, Jan Hasenauer, Falk Schreiber, Andreas Dräger, Emek Demir, Olaf Wolkenhauer, Laura I. Furlong, Emmanuel Barillot, Joaquin Dopazo, Aurelio Orta-Resendiz, Francesco Messina, Alfonso Valencia, Akira Funahashi, Hiroaki Kitano, Charles Auffray, Rudi Balling, Reinhard Schneider Scientific Data, 2020
The ELIXIR Human Copy Number Variations Community: Building bioinformatics infrastructure for research David Salgado, Irina M. Armean, Michael Baudis, Sergi Beltran, Salvador Capella-Gutierrez, Denise Carvalho-Silva, Victoria Dominguez Del Angel, Joaquin Dopazo, Laura I. Furlong, Bo Gao, Leyla Garcia, Dietlind Gerloff, Ivo Gut, Attila Gyenesei, Nina Habermann, John M. Hancock, Marc Hanauer, Eivind Hovig, Lennart F. Johansson, Thomas Keane, Jan Korbel, Katharina B. Lauer, Steve Laurie, Brane Leskošek, David Lloyd, Tomas Marques-Bonet, Hailiang Mei, Katalin Monostory, Janet Piñero, Krzysztof Poterlowicz, Ana Rath, Pubudu Samarakoon, Ferran Sanz, Gary Saunders, Daoud Sie, Morris A. Swertz, Kirill Tsukanov, Alfonso Valencia, Marko Vidak, Cristina Yenyxe González, Bauke Ylstra, Christophe Béroud F1000research, 2020
Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case–control approaches Paulina Gomez‐Rubio, Janet Piñero, Esther Molina‐Montes, Alba Gutiérrez‐Sacristán, Mirari Marquez, Marta Rava, Christoph W. Michalski, Antoni Farré, Xavier Molero, Matthias Löhr, José Perea, William Greenhalf, Michael O'Rorke, Adonina Tardón, Thomas Gress, Victor M. Barberá, Tatjana Crnogorac‐Jurcevic, Luís Muñoz‐Bellvís, Enrique Domínguez‐Muñoz, Joaquim Balsells, Eithne Costello, Jingru Yu, Mar Iglesias, Lucas Ilzarbe, Jörg Kleeff, Bo Kong, Josefina Mora, Liam Murray, Damian O'Driscoll, Ignasi Poves, Rita T. Lawlor, Weimin Ye, Manuel Hidalgo, Aldo Scarpa, Linda Sharp, Alfredo Carrato, Francisco X. Real, Laura I. Furlong, Núria Malats, and International Journal of Cancer, 2019
The BIOMEPOC Project: Personalized Biomarkers and Clinical Profiles in Chronic Obstructive Pulmonary Disease Joaquim Gea, Sergi Pascual, Ady Castro-Acosta, Carmen Hernández-Carcereny, Robert Castelo, Eduardo Márquez-Martín, Concepción Montón, Alexandre Palou, Rosa Faner, Laura I. Furlong, Luis Seijo, Ferran Sanz, Montserrat Torà, Carles Vilaplana, Carme Casadevall, José Luis López-Campos, Eduard Monsó, Germán Peces-Barba, Borja G. Cosío, Alvar Agustí, Mireia Admetlló, Alvar Agustí, Carlos Alvarez-Martínez, Esther Barreiro, Carme Casadevall, Ferran Casals, Robert Castelo, Ady Castro-Acosta, Rocío Córdova, Borja G. Cosío, Rosa Faner, Laura I. Furlong, Marian García, Joaquim Gea, José G. González-García, Carmen Hernández-Carcereny, José Luis López-Campos, Eduardo Márquez, Eduard Monsó, Concepción Montón, Miren Josune Ormaza, Alexandre Palou, Sergi Pascual, Germán Peces-Barba, Pau Puigdevall, Ferran Sanz, Luis Seijó, Montserrat Torà, Yolanda Torralba, Carles Vilaplana Archivos De Bronconeumologia, 2019
Supervised learning approaches to detect negation cues in Spanish reviews Ceur Workshop Proceedings, 2019
Nanopublications: A growing resource of provenance-centric scientific linked data Tobias Kuhn, Juan M. Banda, Egon Willighagen, Friederike Ehrhart, Chris Evelo, Tareq B. Malas, Michel Dumontier, Albert Merono-Penuela, Alexander Malic, Jorrit H. Poelen, Allen H. Hurlbert, Emilio Centeno Ortiz, Laura I. Furlong, Nuria Queralt-Rosinach, Christine Chichester Proceedings IEEE 14th International Conference on Escience E Science 2018, 2018
A systems approach identifies time-dependent associations of multimorbidities with pancreatic cancer risk P. Gomez-Rubio, V. Rosato, M. Márquez, C. Bosetti, E. Molina-Montes, M. Rava, J. Piñero, C.W. Michalski, A. Farré, X. Molero, M. Löhr, L. Ilzarbe, J. Perea, W. Greenhalf, M. O’Rorke, A. Tardón, T. Gress, V.M. Barberá, T. Crnogorac-Jurcevic, L. Muñoz-Bellvís, E. Domínguez-Muñoz, A. Gutiérrez-Sacristán, J. Balsells, E. Costello, C. Guillén-Ponce, J. Huang, M. Iglesias, J. Kleeff, B. Kong, J. Mora, L. Murray, D. O’Driscoll, P. Peláez, I. Poves, R.T. Lawlor, A. Carrato, M. Hidalgo, A. Scarpa, L. Sharp, L.I. Furlong, F.X. Real, C. La Vecchia, N. Malats Annals of Oncology, 2017
Reliable granular references to changing linked data Tobias Kuhn, Egon Willighagen, Chris Evelo, Núria Queralt-Rosinach, Emilio Centeno, Laura I. Furlong Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
Text mining and expert curation to develop a database on psychiatric diseases and their genes Alba Gutiérrez-Sacristán, Àlex Bravo, Marta Portero-Tresserra, Olga Valverde, Antonio Armario, M.C. Blanco-Gandía, Adriana Farré, Lierni Fernández-Ibarrondo, Francina Fonseca, Jesús Giraldo, Angela Leis, Anna Mané, M.A. Mayer, Sandra Montagud-Romero, Roser Nadal, Jordi Ortiz, Francisco Javier Pavon, Ezequiel Jesús Perez, Marta Rodríguez-Arias, Antonia Serrano, Marta Torrens, Vincent Warnault, Ferran Sanz, Laura I. Furlong Database, 2017
Publishing DisGeNET as nanopublications Núria Queralt-Rosinach, Tobias Kuhn, Christine Chichester, Michel Dumontier, Ferran Sanz, Laura I. Furlong Semantic Web, 2016
Text mining and expert curation to develop a database on psychiatric diseases and their genes Ceur Workshop Proceedings, 2016
Molecular and clinical diseasome of comorbidities in exacerbated COPD patients Rosa Faner, Alba Gutiérrez-Sacristán, Ady Castro-Acosta, Solène Grosdidier, Wenqi Gan, Milagros Sánchez-Mayor, Jose Luis Lopez-Campos, Francisco Pozo-Rodriguez, Ferran Sanz, David Mannino, Laura I. Furlong, Alvar Agusti European Respiratory Journal, 2015
Personalized respiratory medicine: Exploring the horizon, addressing the issues: Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014 Alvar Agustí, Josep Maria Antó, Charles Auffray, Ferran Barbé, Esther Barreiro, Jordi Dorca, Joan Escarrabill, Rosa Faner, Laura I. Furlong, Judith Garcia-Aymerich, Joaquim Gea, Bertil Lindmark, Eduard Monsó, Vicente Plaza, Milo A. Puhan, Josep Roca, Juan Ruiz-Manzano, Laura Sampietro-Colom, Ferran Sanz, Luis Serrano, James Sharpe, Oriol Sibila, Edwin K. Silverman, Peter J. Sterk, Jacob I. Sznajder American Journal of Respiratory and Critical Care Medicine, 2015
Exposing provenance metadata using different RDF models Ceur Workshop Proceedings, 2015
Reuse of EHRs to Support Clinical Research in a Hospital of Reference Mayer Miguel A., Furlong Laura I., Torre Pilar, Planas Ignasi, Cots Francesc, Izquierdo Elisabet, Portabella Jordi, Rovira Javier, Gutierrez-Sacristan Alba, Sanz Ferran Studies in Health Technology and Informatics, 2015
Network medicine analysis of COPD multimorbidities Solène Grosdidier, Antoni Ferrer, Rosa Faner, Janet Piñero, Josep Roca, Borja Cosío, Alvar Agustí, Joaquim Gea, Ferran Sanz, Laura I Furlong Respiratory Research, 2014
The semanticscience integrated ontology (SIO) for biomedical research and knowledge discovery Michel Dumontier, Christopher JO Baker, Joachim Baran, Alison Callahan, Leonid Chepelev, José Cruz-Toledo, Nicholas R Del Rio, Geraint Duck, Laura I Furlong, Nichealla Keath, Dana Klassen, Jamie P McCusker, Núria Queralt-Rosinach, Matthias Samwald, Natalia Villanueva-Rosales, Mark D Wilkinson, Robert Hoehndorf Journal of Biomedical Semantics, 2014
BeFree: A text mining system to extract relations between genes, diseases and drugs for translational research Smbm 2014 Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine, 2014
Gathering and exploring scientific knowledge in pharmacovigilance Pedro Lopes, Tiago Nunes, David Campos, Laura Ines Furlong, Anna Bauer-Mehren, Ferran Sanz, Maria Carmen Carrascosa, Jordi Mestres, Jan Kors, Bharat Singh, Erik van Mulligen, Johan Van der Lei, Gayo Diallo, Paul Avillach, Ernst Ahlberg, Scott Boyer, Carlos Diaz, José Luís Oliveira Plos One, 2013
Drug-Induced Acute Myocardial Infarction: Identifying 'Prime Suspects' from Electronic Healthcare Records-Based Surveillance System Preciosa M. Coloma, Martijn J. Schuemie, Gianluca Trifirò, Laura Furlong, Erik van Mulligen, Anna Bauer-Mehren, Paul Avillach, Jan Kors, Ferran Sanz, Jordi Mestres, José Luis Oliveira, Scott Boyer, Ernst Ahlberg Helgee, Mariam Molokhia, Justin Matthews, David Prieto-Merino, Rosa Gini, Ron Herings, Giampiero Mazzaglia, Gino Picelli, Lorenza Scotti, Lars Pedersen, Johan van der Lei, Miriam Sturkenboom, on behalf of the EU-ADR consortium Plos One, 2013
The EU-ADR Web Platform: Delivering advanced pharmacovigilance tools José Luis Oliveira, Pedro Lopes, Tiago Nunes, David Campos, Scott Boyer, Ernst Ahlberg, Erik M. van Mulligen, Jan A. Kors, Bharat Singh, Laura I. Furlong, Ferran Sanz, Anna Bauer‐Mehren, Maria C. Carrascosa, Jordi Mestres, Paul Avillach, Gayo Diallo, Carlos Díaz Acedo, Johan van der Lei Pharmacoepidemiology and Drug Safety, 2013
DisGeNET RDF: A gene-disease association Linked open data resource Ceur Workshop Proceedings, 2013
Improving data and knowledge management to better integrate health care and research M. Cases, L. I. Furlong, J. Albanell, R. B. Altman, R. Bellazzi, S. Boyer, A. Brand, A. J. Brookes, S. Brunak, T. W. Clark, J. Gea, P. Ghazal, N. Graf, R. Guigó, T. E. Klein, N. López‐Bigas, V. Maojo, B. Mons, M. Musen, J. L. Oliveira, A. Rowe, P. Ruch, A. Shabo, E. H. Shortliffe, A. Valencia, J. van der Lei, M. A. Mayer, F. Sanz Journal of Internal Medicine, 2013
DisGeNET: From MySQL to nanopublication, modelling gene-disease associations for the semantic web Ceur Workshop Proceedings, 2012
Automatic filtering and substantiation of drug safety signals Anna Bauer-Mehren, Erik M. van Mullingen, Paul Avillach, María del Carmen Carrascosa, Ricard Garcia-Serna, Janet Piñero, Bharat Singh, Pedro Lopes, José L. Oliveira, Gayo Diallo, Ernst Ahlberg Helgee, Scott Boyer, Jordi Mestres, Ferran Sanz, Jan A. Kors, Laura I. Furlong Plos Computational Biology, 2012
Assessment of NER solutions against the first and second CALBC Silver Standard Corpus Dietrich Rebholz-Schuhmann, Antonio Yepes, Chen Li, Senay Kafkas, Ian Lewin, Ning Kang, Peter Corbett, David Milward, Ekaterina Buyko, Elena Beisswanger, Kerstin Hornbostel, Alexandre Kouznetsov, René Witte, Jonas B Laurila, Christopher JO Baker, Cheng-Ju Kuo, Simone Clematide, Fabio Rinaldi, Richárd Farkas, György Móra, Kazuo Hara, Laura I Furlong, Michael Rautschka, Mariana Neves, Alberto Pascual-Montano, Qi Wei, Nigel Collier, Md Chowdhury, Alberto Lavelli, Rafael Berlanga, Roser Morante, Vincent Van Asch, Walter Daelemans, José Marina, Erik van Mulligen, Jan Kors, Udo Hahn Journal of Biomedical Semantics, 2011
Knowledge environments representing molecular entities for the virtual physiological human Martin Hofmann-Apitius, Juliane Fluck, Laura Furlong, Oriol Fornes, Corinna Kolářik, Susanne Hanser, Martin Boeker, Stefan Schulz, Ferran Sanz, Roman Klinger, Theo Mevissen, Tobias Gattermayer, Baldo Oliva, Christoph M Friedrich Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 2008
Identifying gene-specific variations in biomedical text ROMAN KLINGER, CHRISTOPH M. FRIEDRICH, HEINZ THEODOR MEVISSEN, JULIANE FLUCK, MARTIN HOFMANN-APITIUS, LAURA I. FURLONG, FERRAN SANZ Journal of Bioinformatics and Computational Biology, 2007
