Publications

2012

Beatriz García Jiménez. "Functional Annotation of Proteins based on Relational Representatiton in the Systems Biology Context." Doctoral Thesis. June 4. 2012. Universidad Carlos III de Madrid. [PDF]

Functional annotation is an open and interesting research topic in Molecular Biology. Determining a function in terminology terms is a hard task, due to lack of unified criterion and also because a function takes up many levels for the same protein. Given this difficulties, the way to determine a protein function is to annotate it with several terms from different vocabularies. Proteins carry out their function together with other proteins, being part of protein complexes. These interactions are represented in a network of experimentally verified protein-protein interactions. Analyzing and using the interaction network is task of interest due to the great number of associations, and to the multiple ways in which a protein could influence in the function of others. Therefore, this thesis focuses in the prediction of functional annotation based on networks. It's apparent that this complex scenario couldn't be faced without computational techniques. In fact, in Computational Biology, there is a considerable activity specially devoted to this topic. This thesis is part of this effort for applying computational methods to biological problems in the Systems Biology area. This approximation can belong to the Systems Biology context, because it does not analyze function in an isolated way for each molecule, but at system level, taking into account all the relations among genes and proteins linked at different levels. To take advantages of all these biological relations, and to preserve their structured semantics, this thesis suggests to use Relational Representation, since in particular it is suitable for the concerning domain. Over such representation, multiple transformations and Artificial Intelligence techniques are applied to retrieve implicit knowledge from the related proteins, and to propose new functions through the prediction of functional associations between proteins. The main proposal of this thesis is to characterize the function of proteins and genes based on networks, through Relational Representation and Machine Learning. Specially, from a relational representation specific to functional annotation, we look for the computational design needed to solve two specific, biological interesting and different problems. The former consists of predicting functional association between pair of proteins in E.coli, and the latter comprises expanding pathways in humans. We perform an assessment in computational and biological interpretation terms. Besides, we propose new putative protein functional annotations to be experimentally verified. In addition, the thesis investigates diverse approaches to knowledge representation and learning techniques, suggesting specific strategies to tackle other biological problems, specially where relational data or multi-class and multi-label targets are present.

Best thesis award by the Royal Academy of Spanish Ph.Doctors (Experimental and Technological Sciences, 2012)
Media appearance
Beatriz García-Jiménez, Tirso Pons, Araceli Sanchis and Alfonso Valencia. "Relational Learning-based Extension for Reactome Pathways with Sequence Features and Interactions." 11th Spanish Symposium on Bioinformatics. JBI 2012. January 23-25. Barcelona, Spain. Oral presentation. [URL] [PDF] [BibTeX]

2011

Beatriz García-Jiménez, Agapito Ledezma and Araceli Sanchis. "MMRF for Proteome Annotation Applied to Human Protein Disease Prediction." 20th International Conference on Inductive Logic Programming. ILP 2010. June 27-30. Florence, Italy. Revised Papers. 2011. Lecture Notes in Artificial Intelligence Series, Vol. 6489. pp. 67-75. Springer. ISBN 978-3-642-21294-9; doi: 10.1007/978-3-642-21295-6_11. [URL] [PDF] [Suppl.Material] [BibTeX]

2010

Beatriz García-Jiménez, David Juan, Iakes Ezkurdia, Eduardo Andres-León, Alfonso Valencia. "Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach." PLoS ONE 5(4): e9969. PLoS ONE 2010; doi: 10.1371/journal.pone.0009969. [URL] [PDF] [Citation]
Beatriz García Jiménez, Agapito Ledezma and Araceli Sanchis. "S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework." 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems. IEA/AIE 2010. June 1-4. Córdoba, Spain. Lecture Notes in Artificial Intelligence Series, Vol. 6098. pp. 82-91. Springer. [PDF] [BibTeX]

2009

Beatriz García Jiménez, Agapito Ledezma and Araceli Sanchis. "Modular Multi-Relational Framework for Gene Group Function Prediction." 19th International Conference on Inductive Logic Programming. ILP 2009. July 2-4. Leuven, Belgium. Poster. [URL] [PDF] [BibTeX]
Eduardo Andres León, Iakes Ezkurdia, Beatriz García, Alfonso Valencia, David Juan. "EcID. A database for the inference of functional interactions in E.coli." Nucleic Acids Research. NAR 2009, Vol. 37, Database issue D629-D635; doi: 10.1093/nar/gkn853. [URL] [PDF] [BibTeX]

2008

Beatriz Garcia, Ricardo Aler, Agapito Ledezma and Araceli Sanchis. "Genetic Programming for Predicting Protein Networks." Proceedings of the 11th Ibero-American Conference on Artificial Intelligence. IBERAMIA 2008. October 14-17. Lisbon, Portugal. Lecture Notes in Artificial Intelligences Series, Vol. 5290. pp. 432-441. Springer. [PDF] [BibTeX]
Beatriz Garcia, Ricardo Aler, Agapito Ledezma and Araceli Sanchis. "Protein-Protein Functional Association Prediction Using Genetic Programming." Proceedings of the International Conference on Genetic and Evolutionary Computation. GECCO 2008. July 12-16. Atlanta, Georgia, USA. ACM. pp. 347-348. Poster. [PDF] [BibTeX]

2003

Germán Gutiérrez, Beatriz García, José M. Molina and Araceli Sanchis. "Studying the Capacity of Grammatical Encoding to Generate FNN Architectures." Computational Methods in Neural Modeling. Proceedings of the 7th International Work-Conference on Artificial and Natural Neuronal Networks. IWANN 2003. June 3-6. Maó, Menorca, Spain. Lecture Notes in Computer Science Series, Vol. 2686. pp. 478-485. Springer. [PDF] [BibTeX]

Research Interests

My research interests are mainly Bioinformatics and Machine Learning: solving problems in molecular biology domain applying artificial intelligence techniques:

Bioinformatics.
Machine Learning.
Gene List Function Prediction.
Protein-Protein Interaction Prediction.
Data Mining.
Relational Data Mining.
Evolutionary Computation (Genetic Programming).
Ensemble Classifiers.
Multiple Data Sources Integration.
Computational Representation of Biological Data.