Francisco Azuaje - researchr alias

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Viewing Publication 1 - 83 from 83

2022

DrDimont: explainable drug response prediction from differential analysis of multi-omics networksPauline Hiort, Julian Hugo, Justus Zeinert, Nataniel Müller, Spoorthi Kashyap, Jagath C. Rajapakse, Francisco Azuaje, Bernhard Y. Renard, Katharina Baum. bioinformatics, 38(Supplement_2), 2022. [doi]

2020

A Network-Based Embedding Method for Drug-Target Interaction PredictionPoorya Parvizi, Francisco Azuaje, Evropi Theodoratou, Saturnino Luz. embc 2020: 5304-5307 [doi]

2017

Computational models for predicting drug responses in cancer researchFrancisco Azuaje. bib, 18(5):820-829, 2017. [doi]

2014

An information theoretic approach to assessing gene-ontology-driven similarity and its applicationHaiying Wang, Francisco Azuaje, Huiru Zheng. ijdmb, 9(2):121-134, 2014. [doi]

2013

Detection of protein complexes in protein interaction networks is improved through network-driven functional homogeneity analysisHaiying Wang, Huiru Zheng, Francisco Azuaje. bibm 2013: 43-48 [doi]

Correlating adverse drug reactions with biological pathways in humansHuiru Zheng, Haiying Wang, Hua Xu, Zhongming Zhao, Francisco Azuaje. bibm 2013: 197-200 [doi]

2012

2011

Computational discrete models of tissue growth and regenerationFrancisco Azuaje. bib, 12(1):64-77, 2011. [doi]

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular diseaseFrancisco Azuaje, Huiru Zheng, Anyela Camargo, Haiying Wang. jbi, 44(4):637-647, 2011. [doi]

Predictive integration of gene functional similarity and co-expression defines treatment response of endothelial progenitor cellsFrancisco Azuaje, Haiying Wang, Huiru Zheng, Frédérique Léonard, Magali Rolland-Turner, Lu Zhang, Yvan Devaux, Daniel R. Wagner. bmcsb, 5:46, 2011. [doi]

Prognostic transcriptional association networks: a new supervised approach based on regression treesIsabel A. Nepomuceno-Chamorro, Francisco Azuaje, Yvan Devaux, Petr V. Nazarov, Arnaud Muller, Jesús S. Aguilar-Ruiz, Daniel R. Wagner. bioinformatics, 27(2):252-258, 2011. [doi]

2010

Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction networkFrancisco Azuaje, Yvan Devaux, Daniel R. Wagner. bmcsb, 4:60, 2010. [doi]

Identification of potential targets in biological signalling systems through network perturbation analysisFrancisco Azuaje, Yvan Devaux, Daniel R. Wagner. biosystems, 100(1):55-64, 2010. [doi]

Transcriptional networks characterize ventricular dysfunction after myocardial infarction: A proof-of-concept investigationFrancisco Azuaje, Yvan Devaux, Melanie Vausort, Céline Yvorra, Daniel R. Wagner. jbi, 43(5):812-819, 2010. [doi]

Multi-level integrative analysis of Protein - Protein Interaction networks: connecting completeness, depth and robustnessJaine K. Blayney, Huiru Zheng, Haiying Wang, Francisco Azuaje. ijcbdd, 3(1):31-51, 2010. [doi]

A knowledge-driven probabilistic framework for the prediction of protein-protein interaction networksFiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje. cbm, 40(3):306-317, 2010. [doi]

From Experimental Approaches to Computational Techniques: A Review on the Prediction of Protein-Protein InteractionsFiona Browne, Huiru Zheng, Haiying Wang, Francisco Azuaje. advai, 2010, 2010. [doi]

Integration of Gene Ontology-based similarities for supporting analysis of protein-protein interaction networksHaiying Wang, Huiru Zheng, Fiona Browne, David H. Glass, Francisco Azuaje. prl, 31(14):2073-2082, 2010. [doi]

Ontology- and graph-based similarity assessment in biological networksHaiying Wang, Huiru Zheng, Francisco Azuaje. bioinformatics, 26(20):2643-2644, 2010. [doi]

eNelator: A simulation system for large-scale vulnerability analysis of species-, disease- and process-specific protein networksHuiru Zheng, Haiying Wang, Francisco Azuaje. jocs, 1(4):197-205, 2010. [doi]

seGOsa: Software environment for gene ontology-driven similarity assessmentHuiru Zheng, Francisco Azuaje, Haiying Wang. bibm 2010: 539-542 [doi]

2009

Computational biology for cardiovascular biomarker discoveryFrancisco Azuaje, Yvan Devaux, Daniel Wagner. bib, 10(4):367-377, 2009. [doi]

Assessing the Most Effective Depth for PPI AnalysisJaine K. Blayney, Huiru Zheng, Haiying Wang, Francisco Azuaje. ijcbs 2009: 286-292 [doi]

Assessing the impact of network depth on the analysis of PPI networks: A case studyJaine K. Blayney, Haiying Wang, Huiru Zheng, Francisco Azuaje. cibcb 2009: 200-207 [doi]

GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction predictionFiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje. scfbm, 4:2, 2009. [doi]

Machine Learning Techniques for the Automated Classification of Adhesin-Like Proteins in the Human Protozoan Parasite Trypanosoma cruziAna M. GonzÃ¡lez, Francisco Azuaje, Jose L. Ramirez, Jose F. da Silveira, JosÃ© R. Dorronsoro. tcbb, 6(4):695-702, 2009. [doi]

A standard-compliant global map of the bile acid/xenobiotic signaling network: Construction and automated query processingOliver Schmidt, Jose LÃ³pez, Francisco Azuaje, Paul Thompson, Martin Swain, Werner Dubitzky. biocomp 2009: 657-663

2008

Computational prediction of protein interaction networks through supervised classification techniquesFiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje. ijfipm, 1(2):205-221, 2008. [doi]

Reassessing the limit of data integration for the prediction of protein-protein interactions in Saccharomyces cerevisiaeFiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje. cibcb 2008: 128-135 [doi]

Permutation - based statistical tests for multiple hypothesesAnyela Camargo, Francisco Azuaje, Haiying Wang, Huiru Zheng. scfbm, 3, 2008. [doi]

Special Section on Machine Intelligence Approaches to Systems BiologyGary B. Fogel, Eric Bullinger, S. F. Su, Francisco Azuaje. tsmc, 38(1):2-4, 2008. [doi]

Clustering-based approaches to SAGE data miningHaiying Wang, Huiru Zheng, Francisco Azuaje. biodatamining, 1, 2008. [doi]

Improving Pattern Discovery and Visualization of SAGE Data Through Poisson-Based Self-Adaptive Neural NetworksHuiru Zheng, Haiying Wang, Francisco Azuaje. titb, 12(4):459-469, 2008. [doi]

2007

Supervised Statistical and Machine Learning Approaches to Inferring Pairwise and Module-Based Protein Interaction NetworksFiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje. bibe 2007: 1365-1369 [doi]

Integration and analysis of heterogeneous microarray data sources for supporting drug target identification in atherosclerosisAnyela Camargo, Francisco Azuaje. bmcsb, 1(S-1), 2007. [doi]

Inferring Adaptive Regulation Thresholds and Association Rules from Gene Expression Data through Combinatorial Optimization LearningIgnacio Ponzoni, Francisco Azuaje, Juan Augusto, David Glass. tcbb, 4(4):624-634, 2007. [doi]

Poisson-Based Self-Organizing Feature Maps and Hierarchical Clustering for Serial Analysis of Gene Expression DataHaiying Wang, Huiru Zheng, Francisco Azuaje. tcbb, 4(2):163-175, 2007. [doi]

Self-Adaptive Neural Networks Based on a Poisson Approach for Knowledge DiscoveryHaiying Wang, Huiru Zheng, Francisco Azuaje. IJCAI 2007: 1101-1106 [doi]

2006

Predictive Integration of Gene Ontology-Driven Similarity and Functional InteractionsFrancisco Azuaje, Haiying Wang, Huiru Zheng, Olivier Bodenreider, Alban Chesneau. icdm 2006: 114-119 [doi]

Incorporating Biological Domain Knowledge into Cluster Validity AssessmentNadia Bolshakova, Francisco Azuaje, Padraig Cunningham. evoW 2006: 13-22 [doi]

An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactionsFiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje. jib, 3(2), 2006. [doi]

A Supervised Learning Approach to Predicting Coronary Heart Disease Complications in Type 2 Diabetes Mellitus PatientsMarisol Giardina, Francisco Azuaje, Paul J. McCullagh, Roy Harper. bibe 2006: 325-331

Semi-Supervised Clustering Models for Clinical Risk AssessmentYongyang Huo, Francisco Azuaje, Paul J. McCullagh, Roy Harper. bibe 2006: 243-250

An assessment of recently published gene expression data analyses: reporting experimental design and statistical factorsPeyman Jafari, Francisco Azuaje. midm, 6:27, 2006. [doi]

Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression dataHaiying Wang, Huiru Zheng, David Simpson, Francisco Azuaje. bmcbi, 7:116, 2006. [doi]

Computational Approaches to Supporting Large-Scale Analysis of Photoreceptor-Enriched Gene ExpressionHaiying Wang, Huiru Zheng, Francisco Azuaje. cbms 2006: 533-538 [doi]

2005

Integrative data analysis for functional prediction: a multi-objective optimization approachFrancisco Azuaje. bioinformatics, 21(9):2099-2100, 2005. [doi]

Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and ChallengesFrancisco Azuaje, Joaquín Dopazo. In Francisco Azuaje, Joaquín Dopazo, editors, Data Analysis and Visualization in Genomics and Proteomics. pages 1-9, Wiley, 2005. [doi]

Non-linear mapping for exploratory data analysis in functional genomicsFrancisco Azuaje, Haiying Wang, Alban Chesneau. bmcbi, 6:13, 2005. [doi]

Data and Predictive Model Integration: An Overview of Key Concepts, Problems and SolutionsFrancisco Azuaje, Joaquín Dopazo, Haiying Wang. In Francisco Azuaje, Joaquín Dopazo, editors, Data Analysis and Visualization in Genomics and Proteomics. pages 29-39, Wiley, 2005. [doi]

An integrated tool for microarray data clustering and cluster validity assessment Nadia Bolshakova, Francisco Azuaje, Padraig Cunningham. bioinformatics, 21(4):451-455, 2005. [doi]

A knowledge-driven approach to cluster validity assessmentNadia Bolshakova, Francisco Azuaje, Padraig Cunningham. bioinformatics, 21(10):2546-2547, 2005. [doi]

A Missing Data Estimation Analysis in Type II Diabetes DatabasesMarisol Giardina, Yongyang Huo, Francisco Azuaje, Paul J. McCullagh, Roy Harper. cbms 2005: 347-352 [doi]

An Ontology-Driven Clustering Method for Supporting Gene Expression AnalysisHaiying Wang, Francisco Azuaje, Olivier Bodenreider. cbms 2005: 389-394 [doi]

2004

Data Mining Approaches to Diffuse Large B-Cell Lymphoma Gene Expression Data InterpretationJesús S. Aguilar-Ruiz, Francisco Azuaje, José Cristóbal Riquelme Santos. dawak 2004: 279-288 [doi]

Knowledge Discovery in Lymphoma Cancer from Gene-ExpressionJesús S. Aguilar-Ruiz, Francisco Azuaje. ideal 2004: 31-38 [doi]

Incorporating Ontology-Driven Similarity Knowledge into Functional Genomics: An Exploratory StudyFrancisco Azuaje, Olivier Bodenreider. bibe 2004: 317-324 [doi]

Reply to comments on A computational evolutionary approach to evolving game strategy and Cooperation Francisco Azuaje. tsmc, 34(3):1573, 2004. [doi]

An integrated tool for microarray data clustering and cluster validity assessmentNadia Bolshakova, Francisco Azuaje, Padraig Cunningham. SAC 2004: 133-137 [doi]

A Non-Random Data Sampling Method for Classification Model AssessmentDan Sprevak, Francisco Azuaje, Haiying Wang. icpr 2004: 406-409 [doi]

An integrative and interactive framework for improving biomedical pattern discovery and visualizationHaiying Wang, Francisco Azuaje, Norman D. Black. titb, 8(1):16-27, 2004.

Interactive GSOM-Based Approaches for Improving Biomedical Pattern Discovery and VisualizationHaiying Wang, Francisco Azuaje, Norman D. Black. cis 2004: 556-561 [doi]

Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationshipsHaiying Wang, Francisco Azuaje, Olivier Bodenreider, Joaquín Dopazo. cibcb 2004: 25-31 [doi]

2003

An Immune-inspired Approach to Learning and ClassificationFrancisco Azuaje. appinf 2003: 1-6

Clustering-based Approaches to Discovering and Visualising Microarray Data PatternsFrancisco Azuaje. bib, 4(1):31-42, 2003.

Genomic data sampling and its effect on classification performance assessmentFrancisco Azuaje. bmcbi, 4:5, 2003. [doi]

A computational evolutionary approach to evolving game strategy and cooperationFrancisco Azuaje. tsmc, 33(3):498-503, 2003.

Cluster validation techniques for genome expression dataNadia Bolshakova, Francisco Azuaje. sigpro, 83(4):825-833, 2003. [doi]

Machaon CVE: cluster validation for gene expression dataNadia Bolshakova, Francisco Azuaje. bioinformatics, 19(18):2494-2495, 2003.

Editorial - AI in the Life SciencesWerner Dubitzky, Francisco Azuaje. air, 20(1-2):7-11, 2003. [doi]

A markup language for electrocardiogram data acquisition and analysis (ecgML)Haiying Wang, Francisco Azuaje, Benjamin Jung, Norman D. Black. midm, 3:4, 2003. [doi]

2002

A cluster validity framework for genome expression dataFrancisco Azuaje. bioinformatics, 18(2):319-320, 2002.

2001

Adaptation and reverse evolution in a digital ecosystemFrancisco Azuaje. SMC 2001: 1064-1068 [doi]

A computational neural approach to support the discovery of gene function and classes of cancerFrancisco Azuaje. tbe, 48(3):332-339, 2001. [doi]

A Genetic Algorithm and Growing Cell Structure Approach to Learning Case Retrieval StructuresWerner Dubitzky, Francisco Azuaje. In Sankar K. Pal, Tharam S. Dillon, Daniel S. Yeung, editors, Soft Computing in Case Based Reasoning. pages 115-146, Springer, 2001. [doi]

2000

Making genome expression data meaningful: Prediction and discovery of classes of cancer through a connectionist learning approachFrancisco Azuaje. bibe 2000: 208-213

Discovering relevance knowledge in data: a growing cell structures approachFrancisco Azuaje, Werner Dubitzky, Norman D. Black, Kenneth Adamson. tsmc, 30(3):448-460, 2000.

Retrieval strategies for case-based reasoning: a categorised bibliographyFrancisco Azuaje, Werner Dubitzky, Norman D. Black, Kenny Adamson. ker, 15(4):371-379, 2000. [doi]

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