Alberto Martin

Postdoctoral Researcher

“Centro Interdisciplinario de Neurociencia de Valparaíso”.
Research Area: Structural Biology, Machine Learning, Biological networks and graph representation of biological data.
Computational Biology Lab (DLab), Professor Tomas Perez-Acle

Ph.D. in Computer Science, University College of Dublin, Ireland (2009).
Master in Bioinformatics and Computational Biology, Universidad Complutense de Madrid, Spain (2006).
Graduate in Biology, Universidad Autonoma de Madrid, Spain (2003)

Curriculum Vitae

Contact Information:

E-mail: ajmm at dlab.cl
Teléfono: (56 2) 3672000
Fax: (56 2) 2372259
Address: Computational Biology Lab (DLab).
Fundacion Ciencia & Vida.
Avenida Zañartu 1482 Ñuñoa. Santiago. Chile.

Research Statement:

Since the early steps in my scientific career my work has made use of graph representation of biological data combined with machine learning algorithms. Graphs represent networks in which the nodes are biological entities such as genes or proteins and the connections between them represent relationships between these entities. I have used this approach in the development of tools and methods that propose solutions for different problems in biology and more specifically in structural bioinformatics. Some examples of these applications are protein similarity networks,  graph representations of protein structures, and protein mobility inference. Currently my work is more focused on the development of new methods for the inference of gene regulatory networks.

Publications:

Peer-Reviewed Journal Articles:

  • S. Bhakat, A.J.M. Martin and M.E.S. Soliman. “An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of M184V mutation on HIV reverse transcriptase resistance to lamivudine”. Mol. BioSyst., 10(8):2215-2228, 2014

  • M. Giollo ∗ , A.J.M. Martin ∗ , I. Walsh, C. Ferrari and S.C.E. Tosatto. “NeEMO: A Method Using Residue Interaction Networks to Improve Prediction of Protein Stability upon Mutation”. BMC Genomics, 15(Suppl4):S7, 2014. (* shared 1 st authorship)

  • A.J.M. Martin, I. Walsh, T. Di Domenico, I. Micetic and S.C.E. Tosatto. “PANADA: Protein Association Network Annotation, Determination and Analysis” PLoS One, 8(11): e78383, 2013.
  • G. Mazzotta, A. Rossi, E. Leonardi, M. Mason, C. Bertolucci, L. Caccin, B. Spolaore, A.J.M. Martin, M. Schlichting, R. Grebler, C. Helfrich-Forster, S. Mammi, R. Costa and S.C.E. Tosatto. “Fly Cryptochrome and the Visual System”. PNAS, 110(15):6163-8, 2013.
  • T. Di Domenico, I. Walsh, A.J.M. Martin and S.C.E. Tosatto “MobiDB: acomprehensive database of intrinsic protein disorder annotations” Bioinformatics,28(15):2080-1, 2012.
  • I. Walsh, A.J.M. Martin, T. Di Domenico and S.C.E. Tosatto. “ES-pritz: accurate and fast prediction of protein disorder” Bioinformatics, 28(4):503-9, 2012.
  • I. Walsh, A.J.M. Martin, T. Di Domenico, A. Vullo, G. Pollastri and S.C.E. Tosatto. “CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs”. Nucleic Acids Research, 39:W190-196, 2011.
  • A.J.M. Martin, M. Vidotto, F. Boscariol, T. Di Domenico, I. Walsh and S.C.E. Tosatto. “RING: Networking interacting residues, evolutionary information and energetics in protein structures” Bioinformatics, 7(14):2003-2005, 2011.
  • A.J.M. Martin, C. Mirabello and G. Pollastri. “Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment and Ab Initio Protein Folding” Current Protein and Peptide Science, 12(6):549-562,2011.
  • A.J.M. Martin, I. Walsh and S.C.E. Tosatto. “MOBI: a web server to define and visualize structural mobility in NMR protein ensembles”. Bioinformatics, 26(22):2916-2917, 2010.
  • I. Walsh, A.J.M. Martin, C. Mooney, E. Rubagotti, A. Vullo and G. Pollastri. “Ab initio and homology based prediction of protein domains by recursive neural networks”. BMC Bioinformatics, 10:195, 2009.
  • I. Walsh, D. Bau, A.J.M. Martin, C. Mooney, A. Vullo, G. Pollastri. “Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks”. BMC Structural Biology, 9:5, 2009.
  • A.J.M. Martin, D. Bau, I. Walsh, A. Vullo, G. Pollastri. “Long-range information and physicality constraints improve predicted protein contact maps”. Journal of Bioinformatics and Computational Biology, 6(5):1001-20, 2008.
  • G. Pollastri, A.J.M. Martin, C. Mooney, A. Vullo. “Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information”. BMC Bioinformatics, 8:201, 2007.
  • D. Bau, A.J.M. Martin, C. Mooney, A. Vullo, I. Walsh, G. Pollastri. “Distill: A suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins”. BMC Bioinformatics, 7:402, 2006.

Conference Proceedings Articles:

  • A.J.M. Martin, A. Vullo, G. Pollastri. “Neural Network Pairwise Interaction Fields for protein model quality assessment”. Proceedings of the Learning and Intelligent OptimizatioN Conference – LION 3. January 14-18, 2009 – Trento, Italy. Published in Lecture Notes in Computer Science (2009) Springer 5851:235-248. 

Book Chapters:

  • C. Mooney, N, Davey, A.J.M. Martin, I. Walsh, D.C. Shields and G. Pollastri. “Protein Motif Discovery and Structural Analysis“. In-Silico Tools in Gene Discovery in Springer series Methods in Molecular Biology (vol 760). Hinchcliffe, M. and Yu, B. Eds. 2011.
  • A.J.M. Martin, C. Mooney, I. Walsh and G. Pollastri. “Contact Map Prediction by Machine Learning”. In Introduction to Protein Structure Prediction: Methods and Algorithms. H. Rangwala and G. Karypis Eds. John Wiley & Sons, Inc. 2010.

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