WP5 - Systems Biology and Bioinformatics

Working Group (WG)  CDG & Biomedical Bioinformatics

Working Group Leader (WGL): Dr. Laura I. Furlong (head of the Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics (IMIM-UPF) and Associate Professor at the University Pompeu Fabra at Barcelona (Spain).
This working group aims at applying text mining and knowledge management, network biology and real world data analytics to advance biomedical discovery and have an impact in health care and the pharmaceutical industry relevant for CDG.
Results obtained so far from this research project:
  • A project was written. It is dedicated to collect and integrate data on the genes associated to the different CDGs, analyze this information to develop hypothesis on the physiopathology of the disease, and also suggest putative therapeutic interventions.
  • Currently, we are identifying and actively applying for funding to be able to move forward. If you wish to donate for our continuous research efforts please go HERE.
Any student, research groups, researchers, clinicians, patient organisations or other stakeholders who would like to become more involved within this project, are most welcome to get in touch to discuss possible future opportunities and collaborations. Fulfill the contact form available below:

Future opportunities and collaborations:

About Dr. Laura I. Furlong

​
DR Laura I. Furlong

Research Programme on Biomedical Informatics (GRIB)
Hospital del Mar Medical Research Institute (IMIM)
DCEXS, Pompeu Fabra University (UPF)
C/Dr. Aiguader, 88 08003 - Barcelona
Tel.: +34 93 316 0521 Fax: +34 93 316 0550
lfurlong@imim.es
http://ibi.imim.es/LFurlong.html
 
Email: lfurlong@imim.es

​Biography:
Dr. Laura I. Furlong is head of the Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics (IMIM-UPF) and Associate Professor at the University Pompeu Fabra at Barcelona (Spain). She has a PhD in Biology from the University of Buenos Aires and a Msc. in Bioinformatics by University Pompeu Fabra. She has a broad expertise covering molecular biology, computational systems biology and text mining. The goal of her group is to advance biomedical discovery and have an impact in health care and the pharmaceutical industry by applying text mining and knowledge management, network biology and real world data analytics. Her current research lines include: a) development of new strategies and tools for knowledge extraction from biomedical literature; b) network biology for the study of human diseases and drug toxicity; c) bionformatic approaches for the reuse of clinical data for research. Her group also maintains knowledge resources to support translational research, such as the databases DisGeNET and PsyGeNET. She has published over 40 peer-reviewed articles, and act as reviewer for the journals Bioinformatics, BMC Bioinformatics, BMC Systems Biology, Database and PLOS journals. She has participated in several FP7 projects (@neurist, EU-ADR) and is currently involved in the IMI (Innovative Medicines Initiative) projects eTOX, OpenPHACTS, EMIF and IPiE and the H2020 project MedBioinformatics.
Her research will contribute to shed light on the biological mechanisms underlying CDG diseases, their comorbid diseases and help in the identification of therapeutic options for CDG patients by applying data and text mining approaches, and linked data methods.
 
List of 6 publications in the field of CDG:
  1. J. Piñero, A. Berenstein, A. Gonzalez-Perez, A. Chernomoretz, and L. I. Furlong. Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing. Scientific Reports, 6: 24570, 2016. doi: 10.1038/srep24570. This work describes the characterization of different classes of disease genes based on multi-scale properties of the network and the sensitivity of likely deleterious variation, and shades light on current controversies on the network properties of disease genes.
  2. J. Piñero, N. Queralt-Rosinach, À. Bravo, J. Deu-Pons, A. Bauer-Mehren, M. Baron, F. Sanz, and L. I. Furlong. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database, 2015: bav028, 2015. doi: 10.1093/database/bav028. Describes the DisGeNET platform as a knowledge resource on human diseases and their genes, and the suite of tools provided for their use.
  3. À. Bravo, J. Piñero, N. Queralt-Rosinach, M. Rautschka, and L. I. Furlong. Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinformatics, 16(1): 55, 2015. doi: 10.1186/s12859-015-0472-9. This article describes the text mining system BeFree for the identification of relationships between biomedical entities, and explores its application to the identification of disease genes.
  4. L. I. Furlong. Human diseases through the lens of network biology. Trends in Genetics, 29 (3): 150–159, 2013. doi: 10.1016/j.tig.2012.11.004. This article reviews the state of the art on network biology approaches applied to human diseases, highlighting achievements, controverises and the needs not solved at this time.
  5. Bauer-Mehren, E. M. van Mullingen, P. Avillach, M. D. C. Carrascosa, R. Garcia-Serna, J. Piñero, B. Singh, P. Lopes, J. L. Oliveira, G. Diallo, E. Ahlberg Helgee, S. Boyer, J. Mestres, F. Sanz, J. A. Kors, and L. I. Furlong. Automatic filtering and substantiation of drug safety signals. PLoS Computational Biology, 8 (4), 2012. doi: 10.1371/journal.pcbi.1002457. This article describes a new approach to explain why a drug produces a side effect by network analysis of different omics data including information on drug targets, drug metabolites, biological pathways, protein expression and genes associated to the clinical events elicited by the drug.
  6. Bauer-Mehren, M. Bundschus, M. Rautschka, M. A. Mayer, F. Sanz, and L. I. Furlong. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PLoS One, 6 (6), 2011. doi: 10.1371/journal.pone.0020284. This work explores the modularity of functional networks associated to different types of human diseases.​

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