El Grupo de Investigación en Bioinformática y Quimioinformática (BioChemTICs) pertenece a la Universidad Nacional del Sur, una universidad pública de Argentina. BioChemTICs es además parte del Instituto en Ciencias e Ingeniería de la Computación (ICIC), un instituto de investigación del Consejo Nacional de Ciencia y Tecnología de la Argentina (CONICET). BioChemTICs está integrado por seis miembros que trabajan en temáticas de aprendizaje maquinal y minería de datos grandes para abordar aplicaciones en Biología Computacional e Informática Molecular. Nuestras investigaciones se centran en el análisis de cantidades masivas de datos e involucre el uso de métodos de computación evolutiva, redes neuronales artificiales y analítica visual.

Personal del Grupo BioChemTICs

Director: Ignacio Ponzoni

Investigadores: Jessica A. Carballido, Rocío L. Cecchini, Axel J. Soto

Becarios Postdoctorales: María Jimena Martínez

Estudiantes de Doctorado: María Virginia Sabando

Contacto: ip@cs.uns.edu.ar

Publicaciones

  • Cravero, F., Schustik, S., Martínez, M.J., Barranco, C.D., Díaz, M.F., Ponzoni, I. “Computer-Aided Design of Polymeric Materials: Characterization of Databases for Prediction of Mechanical Properties under Polydispersity”. Chemometrics and Intelligent Laboratory Systems, Vol. 191, pp. 65-72 (2019). Elsevier. ISSN: 0169-7439. [doi: 10.1016/j.chemolab.2019.06.006]
  • Ponzoni I., Sebastian-Pérez V., Martínez M.J., Roca C., De la Cruz, C., Cravero F., Vazquez, G.E., Páez J.A., Díaz M.F., Campillo N.E. “QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease”. Scientific Reports, Vol. 9:9102, (2019). Nature Pub. Group. ISSN: 2045-2322. [doi: 10.1038/s41598-019-45522-3]
  • Díaz–Montaña, J.J., Díaz–Díaz, N., Barranco, C.D., Ponzoni, I. “Development and use of a Cytoscape app for GRNCOP2”, Computer Methods and Programs in Biomedicine. Vol. 177, pp. 211–218 (2019). Elsevier. ISSN: 0169-2607. [doi: 10.1016/j.cmpb.2019.05.030]
  • Sebastián-Pérez, V., Martínez, M.J., Gil, C., Campillo, N.E., Martínez, A., Ponzoni, I. “Inference of QSAR Models for identifying LRRK2 inhibitors for the treatment of Parkinson’s Disease», Journal of Integrative Bioinformatics. Vol. 16, Issue 1 (2019). De Gruyter. ISSN: 1613-4516. [doi: 10.1515/jib-2018-0063]
  • Martínez, M.J., Razuc, M., Ponzoni I. “MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies applied to Molecular Informatics”, BioMed International Research. Vol. 2019, Article number 2905203 (2019). Hindawi. ISSN: 2314-6133. [doi: 10.1155/2019/2905203]
  • Cecchini, R.L., Lorenzetti, C.M., Maguitman, A.G., Ponzoni, I. “Topic Relevance and Diversity in Information Retrieval from Large Datasets: A Multi-Objective Evolutionary Algorithm Approach», Applied Soft Computing. Vol. 69, pp. 749-770 (2018). Elsevier. ISSN: 1568-4946. [doi: 10.1016/j.asoc.2017.11.016]
  • Martínez M.J., Dussaut J.S., Ponzoni, I. “Biclustering as Strategy for Improving Feature Selection in Consensus QSAR Modeling”, Electronic Notes in Discrete Mathematics, Vol. 69, pp. 117-124 (2018). Elsevier. ISSN: 1571-0653. [doi: 10.1016/j.endm.2018.07.016]
  • Dussaut J.S., Cecchini, R.L., Gallo, C.A., Ponzoni, I., Carballido, J.A. “A Review of Software Tools for Pathway Crosstalk Inference”, Currents Bioinformatics, 13 (1), pp. 64-72 (2018). Bentham Science.ISSN: 1574-8936. [doi: 10.2174/1574893611666161123123204]
  • Carballido, J.A., Latini M.A., Ponzoni, I., Cecchini, R.L. “An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters”, Electronic Notes in Discrete Mathematics, Vol. 69, pp. 229-236 (2018). Elsevier. ISSN: 1571-0653. [doi: 10.1016/j.endm.2018.07.030]
  • Dussaut J.S., Gallo, C.A., Martínez M.J., Cravero F., Carballido, J.A., Ponzoni, I. “GeRNet: A Gene Regulatory Network Tool”, Biosystems. Vol. 162, pp. 1-11, (2017). Elsevier. ISSN: 0303-2647. [doi: 10.1016/j.biosystems.2017.08.006]
  • Ponzoni I., Sebastian-Pérez, Requena C., Roca C., Martínez M.J., Cravero F., Díaz M.F., Páez J.A., Gomez Arrayas R., Adrio J., Campillo N.E. “Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery”, Scientific Reports. Vol. 7:2403, (2017). Nature Pub. Group. ISSN: 2045-2322. [doi: s41598-017-02114-3]
  • Cravero F., Martínez M.J., Vázquez G., Díaz M., Ponzoni I. “Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design”, Journal of Integrative Bioinformatics. Vol. 13, No. 2, 286 (2016). De Gruyter. ISSN: 1613-4516. [doi: 10.2390/biecoll-jib-2016-286]
  • Romero J.R., Carballido J.A., Garbus I., Echenique V.C., Ponzoni I. “A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching”, Evolutionary Bioinformatics. Vol. 12, pp. 247-251, (2016). SAGE Publishing. ISSN: 1176-9343. [doi: 10.4137/EBO.S40138]
  • Gallo, C.A., Cecchini, R.L., Carballido, J.A., Micheletto, S., Ponzoni, I. “Discretization of gene expression data revised”, Briefings in Bioinformatics. Vol. 17 (5), pp. 758-770, (2016). Oxford University Press. ISSN: 1467-5463. [doi: 10.1093/bib/bbv074]
  • Dussaut J.S., Gallo, C.A., Cecchini, R.L., Carballido, J.A., Ponzoni, I. “Crosstalk Pathway Inference using Topological Information and Biclustering of Gene Expression Data”, Biosystems, Vol.150, pp. 1-12, (2016). Elsevier. ISSN: 0303-2647.[doi: 10.1016/j.biosystems.2016.08.002]
  • Martínez, M.J., Ponzoni, I., Díaz, M.F., Vazquez, G.E., Soto, A.J. “Visual analytics in cheminformatics: user‑supervised descriptor selection for QSAR methods”, Journal of Cheminformatics, Vol. 7, Paper 39, (2015). Springer. ISSN 1758-2946. [doi: 10.1186/s13321-015-0092-4]
  • Carballido, J.A., Gallo, C.A., Dussaut J.S., Ponzoni, I. “On Evolutionary Algorithms for Biclustering of Gene Expression Data”, Currents Bioinformatics, Vol. 10, No. 3, pp. 259-267, (2015). Bentham Science.ISSN: 1574-8936. [doi: 10.2174/1574893609666140829204739].
  • Ponzoni I., Nueda M.J., Tarazona S., Götz S., Montaner D., Dussaut J.S., Dopazo J., Conesa A. “Pathway network inference from gene expression data”, BMC Systems Biology, Vol. 8, S7. Springer Nature, (2014).ISSN: 1752-0509.[doi: 10.1186/1752-0509-8-S2-S7]
  • Romero, J.R., Roncallo, P.F., Akkiraju, P.C., Ponzoni, I., Echenique, V.C., Carballido, J.A. “Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires”, Computers and Electronics in Agriculture, Vol. 96, pp. 173-179. Elsevier, (2013). ISSN: 0168-1699. [doi: 10.1016/j.compag.2013.05.006]
  • Palomba, D., Martínez, M.J., Ponzoni, I., Díaz, M.F., Vazquez, G.E., Soto, A.J. “QSAR models for predicting log Pliver on volatile organic compounds combining statistical methods and domain knowledge”, Molecules, Vol. 17, No. 12, pp. 14937-14953. MDPI AG, (2012). ISSN 1420-3049. [doi: 10.3390/molecules171214937]
  • Cecchini, R.L., Ponzoni, I., Carballido, J.A. “Multi-objective evolutionary approaches for intelligent design of sensor networks in the petrochemical industry”, Expert Systems with Applications, Vol. 39, pp. 2643-2649, Elsevier, (2012). ISSN: 0957-4174. [doi: 10.1016/j.eswa.2011.08.119]
  • Soto, A.J., Vazquez, G.E., Strickert, M., Ponzoni, I. “Target-driven subspace mapping methods and their applicability domain estimation”, Molecular Informatics, Vol. 30, pp. 779–789, Wiley, (2011). ISSN: 1868-1751. [doi: 10.1002/minf.201100053]
  • Gallo, C.A., Carballido, J.A., Ponzoni, I. “Discovering Time-Lagged Rules from Microarray Data using Gene Profile Classifiers”, BMC Bioinformatics. Vol. 12, paper 123, Springer Nature, (2011). ISSN: 1471-2105. [doi: 10.1186/1471-2105-12-123]
  • Soto, A.J., Cecchini, R.J., Vazquez, G.E., Ponzoni, I. “Multi-Objective Feature Selection in QSAR/ QSPR using a Machine Learning Approach”, QSAR & Combinatorial Science. Vol. 28, No. 11-12, pp. 1509-1523. Wiley, (2009). ISSN: 1611-020X. [doi: 10.1002/qsar.200960053]
  • Carballido, J.A., Ponzoni, I., Brignole, N.B. “SID-GA: an Evolutionary Approach for improving Observability and Redundancy Analysis in Structural Instrumentation Design”. Computers & Industrial Engineering. Vol. 56, No. 4, pp. 1419-1428, (2009). Elsevier. ISSN: 0360-8352. [doi: 10.1016/j.cie.2008.09.001]
  • Domancich, A.O., Durante, M., Ferraro, S., Hoch, P., Brignole N.B., Ponzoni I. “How To Improve the Model Partitioning in a DSS for Instrumentation Design”, Industrial & Engineering Chemistry Research. Vol. 48, No. 7, pp. 3513-3525, (2009). American Chemical Society. ISSN: 0888-5885. [doi: 10.1021/ie800449t]
  • Ponzoni, I., Azuaje, F.J., Augusto, J.C., Glass, D.H. “Inferring adaptive regulation thresholds and association rules from gene expression data through combinatorial optimization learning”. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Vol. 4, No. 4, pp. 624-634 (2007). IEEE Computer Society. ISSN: 1545-5963. [doi: 10.1109/tcbb.2007.1049]
  • Carballido, J.A., Ponzoni, I., Brignole, N.B. “CGD-GA: A Graph-based Genetic Algorithm for Sensor Network Design”. Information Sciences. Vol. 177, No. 22, pp. 5091–5102 (2007). Elsevier. ISSN: 0020-0255. [doi: 10.1016/j.ins.2007.05.036]