The artificial intelligence workbencha retrospective review

  1. LÓPEZ-FERNÁNDEZ, Hugo 1
  2. REBOIRO-JATO, Miguel 1
  3. PÉREZ RODRÍGUEZ, José A. 2
  4. FDEZ-RIVEROLA, Florentino 1
  5. GLEZ-PEÑA, Daniel 1
  1. 1 Universidade de Vigo
    info

    Universidade de Vigo

    Vigo, España

    ROR https://ror.org/05rdf8595

  2. 2 CFR: Centro de Formación e Recursos de Ourense
Revista:
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Ano de publicación: 2016

Volume: 5

Número: 1

Páxinas: 73-85

Tipo: Artigo

DOI: 10.14201/ADCAIJ2016517385 DIALNET GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

Resumo

Last decade, biomedical and bioinformatics researchers have been demanding advanced and user-friendly applications for real use in practice. In this context, the Artificial Intelligence Workbench, an open-source Java desktop application framework for scientific software development, emerged with the goal of provid-ing support to both fundamental and applied research in the domain of transla-tional biomedicine and bioinformatics. AIBench automatically provides function-alities that are common to scientific applications, such as user parameter defini-tion, logging facilities, multi-threading execution, experiment repeatability, work-flow management, and fast user interface development, among others. Moreover, AIBench promotes a reusable component based architecture, which also allows assembling new applications by the reuse of libraries from existing projects or third-party software. Ten years have passed since the first release of AIBench, so it is time to look back and check if it has fulfilled the purposes for which it was conceived to and how it evolved over time.

Referencias bibliográficas

  • Alvarado-Pérez, J.C., Peluffo-Ordó-ez, D.H., and Theron, R., 2015, Bridging the Gap between Human Knowledge and Machine Learning. Advances in Distributed Computing and Artificial Intelli-gence Journal, 4(1):54-64. http://dx.doi.org/10.14201/ADCAIJ2015415464
  • Barbosa, P., Dias, O., Arrais, J.P., and Rocha, M., 2014, Metagenomic Analysis of the Saliva Microbiome with Merlin. Advances in Intelligent Systems and Computing, 294:191-199. http://dx.doi.org/10.1007/978-3-319-07581-5_23
  • Dias, D., Rocha, M., Ferreira, E.C., and Rocha, I., 2015, Reconstructing genome-scale metabolic models with merlin. Nucleic Acids Research, 43(8):3899-3910. http://dx.doi.org/10.1093/nar/gkv294
  • Fdez-Riverola, F., Glez-Pe-a, D., López-Fernández, H., Reboiro-Jato, M., and Méndez J.R., 2012, A Java application framework for scientific software development. Software: Practice & Experience, 42/8:1015-1036. http://dx.doi.org/10.1002/spe.1108
  • Galesio, M., López-Fdez, H., Reboiro-Jato, M., Gómez-Meire, S., Glez-Pe-a, D., Fdez-Riverola, F., Lodei-ro, C., Diniz, D., and Capelo, J.L., 2013, Speeding up the screening of steroids in urine: develop-ment of a user-friendly library. Steroids, 78(12-13):1226-1232. http://dx.doi.org/10.1016/j.steroids.2013.08.014
  • Glez-Pe-a, G., Reboiro-Jato, M., Maia, P., Díaz, F., and Fdez-Riverola, F., 2010, AIBench: a rapid appli-cation development framework for translational research in biomedicine. Computer Methods and Programs in Biomedicine, 98(2010):191-203. http://dx.doi.org/10.1016/j.cmpb.2009.12.003
  • Glez-Pe-a, D., Gómez-López, G., Reboiro-Jato, M., Fdez-Riverola, F., and Pisano, D.G., 2011, PileLine: a toolbox to handle genome position information in next-generation sequencing studies. BMC Bio-informatics, 12:31. http://dx.doi.org/10.1186/1471-2105-12-31
  • Gonçalves, E., Rocha, I., and Rocha, M., 2012, Computational tools for strain optimization by tuning the optimal level of gene expression. Advances in Intelligent and Soft Computing, 154:251-258. http://dx.doi.org/10.1007/978-3-642-28839-5_29
  • Hall, M., Frank, E., Geoffrey, H., Pfahringer, B., Reutemann, P., and Witten, I.H., 2009, The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11:10–18. http://dx.doi.org/10.1145/1656274.1656278
  • Khayati, N., and Lejouad-Chaarib, W., 2013. A Distributed and Collaborative Intelligent System for Medical Diagnosis. Advances in Distributed Computing and Artificial Intelligence Journal, 2(2):1-16.
  • López-Fernández, H., Reboiro-Jato, M., Glez-Pe-a, D., Méndez-Reboredo, J.R., Santos, H.M., Carreira, R.J., Capelo, J.L., and Fdez-Riverola, F., 2011a, Rapid development of proteomic applications with the AIBench framework. Journal of Integrative Bioinformatics, 8/3:171.
  • López-Fernández, H., Glez-Pe-a, D., Reboiro-Jato, M., Gómez-López, G., Pisano, D.G., and Fdez-Riverola, F., 2011b, PileLineGUI: a desktop environment for handling genome position files in next-generation sequencing studies. Nucleic Acids Research, 39(suppl. 2):W562-W566. http://dx.doi.org/10.1093/nar/gkr439
  • López-Fernández, H., Glez-Pe-a, D., Reboiro-Jato, M., Aparicio, F., Gachet, D., Buenaga, M., and Fdez-Riverola, F., 2013a, BioAnnote: A software platform for annotating biomedical documents with application in medical learning environments. Computer Methods and Programs in Biomedicine, 111/1:139-147.http://dx.doi.org/10.1016/j.cmpb.2013.03.007
  • López-Fernández, H., Reboiro-Jato, M., Madeira, S.C., López-Cortés, R., Nunes-Miranda, J.D., Santos, H.M., Fdez-Riverola, F., and Glez-Pe-a, D., 2013b, A workflow for the application of biclustering to mass spectrometry data. Advances in Intelligent Systems and Computing, 222: 145-153. http://dx.doi.org/10.1007/978-3-319-00578-2_19
  • López-Fernández, H., Reboiro-Jato, M., Glez-Pe-a, D., and Fdez-Riverola, F., 2014, A comprehensive analysis about the influence of low-level preprocessing techniques on mass spectrometry data for sample classification. International Journal of Data Mining and Bioinformatics, 10/4:455-473.
  • http://dx.doi.org/10.1504/IJDMB.2014.064897
  • López-Fernández, H., Santos, H.M., Capelo, J.L., Fdez-Riverola, F., Glez-Pe-a, D., and Reboiro-Jato, M., 2015, Mass-Up: an all-in-one open software application for MALDI-TOF mass spectrometry knowledge discovery. BMC Bioinformatics, 16:318. http://dx.doi.org/10.1186/s12859-015-0752-4
  • López-Fernández, H., 2016, Application of data mining and artificial intelligence techniques to mass spectrometry data for knowledge discovery. Revista Iberoamericana de Inteligencia Artificial, 19(57):22-25.
  • Lourenço, A., Carreira, R., Carneiro, S., Maia, P., Glez-Pe-a, D., Fdez-Riverola, F., Ferreira, E.C., Rocha, I., and Rocha, M., 2009, @Note: A workbench for Biomedical Text Mining. Journal of Biomedi-cal Informatics, 42(4): 710-720. http://dx.doi.org/10.1016/j.jbi.2009.04.002
  • Noronha, A., Vilaça, P., and Rocha, M., 2013, Network Visualization Tools to Enhance Metabolic Engi-neering Platforms. Advances in Intelligent Systems and Computing, 222:137-144. http://dx.doi.org/10.1007/978-3-319-00578-2_18
  • Nunes-Miranda, J.D., Santos, H.M., Reboiro-Jato, M., Fdez-Riverola, F., Igrejas, G., Lodeiro C., and Capelo, J.L., 2012, Direct matrix assisted laser desorption ionization mass spectrometry-based analysis of wine as a powerful tool for classification purposes. Talanta, 91:72-76. http://dx.doi.org/10.1016/j.talanta.2012.01.017
  • Pereira, A., Felisberto, F., Maduro, L., and Felgueiras, M., 2012. Fall Detection on Ambient Assisted Living using a Wireless Sensor Network. Advances in Distributed Computing and Artificial Intelligence Journal, 1(1):62-77.
  • Pérez-Rodríguez, G., Glez-Pe-a, D., Azevedo, N.F., Pereira, M.O., Fdez-Riverola, F., and Lourenço, A., 2015, Enabling systematic, harmonised and large-scale biofilms data computation: the Biofilms Experiment Workbench. Computer Methods and Programs in Biomedicine, 118/3:309-321. http://dx.doi.org/10.1016/j.cmpb.2014.12.005
  • Reboiro-Jato, D., Reboiro-Jato, M., Fdez-Riverola, F., Vieira, C.P., Fonseca, N.A., and Vieira, J., 2012, ADOPS - Automatic detection of positively selected sites. Journal of Integrative Bioinformatics, 9/3:200.
  • Reboiro-Jato, M., Laza, R., López-Fernández, H., Glez-Pe-a, D., Díaz, F., and Fdez-Riverola, F., 2013, genEnsemble: a new model for the combination of classifiers and integration of biological knowledge applied to genomic data. Expert Systems With Applications, 40/1:52-63. http://dx.doi.org/10.1016/j.eswa.2012.07.003
  • Reboiro-Jato, M., Díaz, F., Glez-Pe-a, D., and Fdez-Riverola, F., 2014, A novel ensemble of classifiers that use biological relevant gene sets for microarray classification. Applied Soft Computing, 17:117-126. http://dx.doi.org/10.1016/j.asoc.2014.01.002
  • Rocha, I., Maia, P., Evangelista, P., Vilaça, P., Soares, S., Pinto, J.P., Nielsen, J., Patil, K.R., Ferreira, E.C., and Rocha, M., 2010, OptFlux: An open-source software platform for in silico metabolic engineer-ing. BMC Systems Biology, 4:45. http://dx.doi.org/10.1186/1752-0509-4-45
  • Rocha, M., Mendes, R., Rocha, O., Rocha, I., and Ferreira, E.C., 2014, Optimization of fed-batch fer-mentation processes with bio-inspired algorithms. Expert Systems With Applications, 41(5): 2186–2195. http://dx.doi.org/10.1016/j.eswa.2013.09.017
  • Romero, R., Seara Vieira, A., Iglesias, E. L., Borrajo, L., 2014, BioClass: A Tool for Biomedical Text Classification. Advances in Intelligent Systems and Computing, 294:243-251. http://dx.doi.org/10.1007/978-3-319-07581-5_29
  • Santos, A., Nogueira, R., and Lourenço, A., 2012. Applying a text mining framework to the extraction of numerical pa-rameters from scientific literature in the biotechnology domain. Advances in Dis-tributed Computing and Artificial Intelligence Journal, 1(1):1-8. http://dx.doi.org/10.1155/2012/312132
  • Santos, H.M., Reboiro-Jato, M., Glez-Pe-a, D., Diniz, M.S., Fdez-Riverola, F., Carvalho R., Lodeiro, C., and Capelo, J.L., 2010, Decision peptide-driven: a free software tool for accurate protein quantifi-cation using gel electrophoresis and matrix assisted laser desorption ionization time of flight mass spectrometry. Talanta, 82/4:1412-1420. http://dx.doi.org/10.1016/j.talanta.2010.07.007
  • Seara Vieira, A., Iglesias, E.L., Borrajo, L., and Romero R., 2014. A HMM Text Classification Model with Learning Capacity. Advances in Distributed Computing and Artificial Intelligence Journal, 3(3):21-34.
  • Spinellis, D., 2008, Software Builders. IEEE Software, 25(3):22-23. http://dx.doi.org/10.1109/MS.2008.74
  • Vilaça, P., Rocha, I., and Rocha, M., 2011, A computational tool for the simulation and optimization of microbial strains accounting integrated metabolic/regulatory information. Biosystems, 103(3):435-441. http://dx.doi.org/10.1016/j.biosystems.2010.11.012
Fonte de datos: Dialnet