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Identification

Personal identification

Full name
Ana Margarida Antunes Coelho Alão de Freitas

Citation names

  • Freitas, Ana Alão

Author identifiers

Ciência ID
5215-238D-58F2
ORCID iD
0000-0001-9454-4874

Email addresses

  • ana.alao@itqb.unl.pt (Professional)

Addresses

  • Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157, Oeiras, Oeiras, Portugal (Professional)

Knowledge fields

  • Exact Sciences - Computer and Information Sciences - Bioinformatics
Education
Degree Classification
2012 - 2014
Concluded
 Mestrado em Bioinformática (Mestrado)
Universidade do Minho, Portugal
"Extraction of Kinetic Information from Literature" (THESIS/DISSERTATION)
 17
2003 - 2008
Concluded
 Licenciatura em Bioinformática (Licenciatura)
Universidade Católica Portuguesa Escola Superior de Biotecnologia, Portugal
 14
Affiliation

Others

Category
Host institution 		Employer
2018/11/01 - Current Aluno Doutoramento Universidade Nova de Lisboa Instituto de Tecnologia Química e Biológica, Portugal
2017/07/19 - 2017/12/31 Bolsa de Investigação (UMINHO/BI/258/2017)), no âmbito do Projecto "DeYeastLib; ERA-IB-2/003/2013". Universidade do Minho Centro de Engenharia Biológica, Portugal
2016/02/01 - 2017/01/31 Bolsa de Investigação (UMINHO/BI/13/2016), no âmbito do Projecto SISBI-Sistema Inteligente de Suporte à Decisão para a Biotecnologia Industrial. Universidade do Minho Centro de Engenharia Biológica, Portugal
2014/12/29 - 2015/07/31 Bolsa de Investigação (UMINHO/BI/388/2014), no âmbito do Projecto "Caracterização dos princípios de design de circuitos metabólicos prevalentes", com a Ref. PTDC/QUI-BIQ/119657/2010-FCOMP-01-0124-FEDE Universidade do Minho Centro de Engenharia Biológica, Portugal
2014/01/01 - 2014/12/11 Projecto/Tese de mestrado, realizada no grupo BioSystems e em colaboração com SilicoLife Lda, com o tema “Extraction of kinetic information from literature” e sob a orientação da Prof. Doutora Isabel Universidade do Minho Centro de Engenharia Biológica, Portugal
2013/03/18 - 2014/03/17 Bolsa de Iniciação Científica (UMINHO/BIC/4/2013), no âmbito do Projecto "Caracterização dos princípios de design de circuitos metabólicos prevalentes", com a Ref. PTDC/QUI_BIQ/119657/2010. Universidade do Minho Centro de Engenharia Biológica, Portugal
2009/02/15 - 2009/07/31 Estágio de mestrado realizado, no âmbito do programa Erasmus, no “Comparative Genomics Group” – CMBI, com o tema: “Analysis of Aspergillus Fumigatus – Solexa sequencing data”, sob a orientação do Prof Radboud Universiteit Centre for Molecular and Biomolecular Informatics, Netherlands
2007/09/01 - 2008/03/05 Projeto final de curso realizado, no âmbito do programa Erasmus, no “Comparative Genomics Group” – CMBI , com o tema: “Evolution of functions in the p53 family – Alignment analysis”, sob a orientação Radboud Universiteit Centre for Molecular and Biomolecular Informatics, Netherlands
Outputs

Publications

Book
  1. Freitas, A.A.; Costa, H.; Rocha, M.; Rocha, I.. A text mining approach for the extraction of kinetic information from literature. 2015.
    10.1007/978-3-319-19776-0_10
Thesis / Dissertation
  1. Freitas, Ana Alão. "Extraction of kinetic information from literature". Master, 2014. http://hdl.handle.net/1822/37497.

Other

Other output
  1. A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer. Purpose Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. Patients and Methods The comparator arms of four phase III clinical trials in first-line mCR. 2019. Prostate Cancer DREAM Challenge Community; Correia, Sara; Freitas, Ana Alão; Rocha, Miguel; Vieira, Vítor. http://hdl.handle.net/1822/59520.
    10.1200/CCI.17.00018
  2. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse E. 2017. Guinney, Justin; Wang, Tao; Laajala, Teemu D.; Winner, Kimberly Kanigel; Bare, J. Christopher; Neto, Elias Chaibub; Khan, Suleiman A.; et al. http://hdl.handle.net/1822/44342.
    10.1016/S1470-2045(16)30560-5
  3. Extracting kinetic information from literature with KineticRE. To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the. 2015. Freitas, Ana Alão; Costa, Hugo; Rocha, Miguel; Rocha, I.. http://hdl.handle.net/1822/38891.
    10.2390/biecoll-jib-2015-282