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  1. Article ; Online: Modeling tumor heterogeneity and predicting effective combined therapies through computational optimization algorithms.

    Azuaje, Francisco J

    Proceedings of the National Academy of Sciences of the United States of America

    2014  Volume 111, Issue 41, Page(s) E4287

    MeSH term(s) Animals ; Antineoplastic Agents/therapeutic use ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Genetic Heterogeneity ; Humans ; Neoplasms/drug therapy ; Neoplasms/pathology
    Chemical Substances Antineoplastic Agents
    Language English
    Publishing date 2014-09-24
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.1414893111
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  2. Article ; Online: Selecting biologically informative genes in co-expression networks with a centrality score.

    Azuaje, Francisco J

    Biology direct

    2014  Volume 9, Page(s) 12

    Abstract: Background: Measures of node centrality in biological networks are useful to detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key disease-related ... ...

    Abstract Background: Measures of node centrality in biological networks are useful to detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key disease-related pathways. Although different approaches to estimating gene centrality are available, their potential biological relevance in gene co-expression networks deserves further investigation. Moreover, standard measures of gene centrality focus on binary interaction networks, which may not always be suitable in the context of co-expression networks. Here, I also investigate a method that identifies potential biologically meaningful genes based on a weighted connectivity score and indicators of statistical relevance.
    Results: The method enables a characterization of the strength and diversity of co-expression associations in the network. It outperformed standard centrality measures by highlighting more biologically informative genes in different gene co-expression networks and biological research domains. As part of the illustration of the gene selection potential of this approach, I present an application case in zebrafish heart regeneration. The proposed technique predicted genes that are significantly implicated in cellular processes required for tissue regeneration after injury.
    Conclusions: A method for selecting biologically informative genes from gene co-expression networks is provided, together with free open software.
    MeSH term(s) Animals ; Gene Expression Profiling/methods ; Gene Regulatory Networks ; Heart/physiology ; Myocardium/metabolism ; Regeneration ; Zebrafish/genetics ; Zebrafish/metabolism ; Zebrafish/physiology
    Language English
    Publishing date 2014-06-19
    Publishing country England
    Document type Evaluation Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1745-6150
    ISSN (online) 1745-6150
    DOI 10.1186/1745-6150-9-12
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  3. Article ; Online: DrDimont: explainable drug response prediction from differential analysis of multi-omics networks.

    Hiort, Pauline / Hugo, Julian / Zeinert, Justus / Müller, Nataniel / Kashyap, Spoorthi / Rajapakse, Jagath C / Azuaje, Francisco / Renard, Bernhard Y / Baum, Katharina

    Bioinformatics (Oxford, England)

    2021  Volume 38, Issue Suppl_2, Page(s) ii113–ii119

    Abstract: Motivation: While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to ... ...

    Abstract Motivation: While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem.
    Results: We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response.
    Availability and implementation: DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Breast Neoplasms/drug therapy ; Female ; Humans ; Proteomics ; Receptors, Estrogen ; Software ; Transcriptome
    Chemical Substances Receptors, Estrogen
    Language English
    Publishing date 2021-10-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btac477
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    Zs.A 2374: Show issues Location:
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  4. Article ; Online: Prediction of adverse cardiovascular events of noncardiovascular drugs through drug-target interaction networks.

    Azuaje, Francisco J / Devaux, Yvan / Wagner, Daniel R

    Clinical and translational science

    2012  Volume 5, Issue 1, Page(s) 111

    MeSH term(s) Cardiovascular Diseases/chemically induced ; Cardiovascular Diseases/metabolism ; Computational Biology ; Data Mining ; Drug-Related Side Effects and Adverse Reactions ; Humans ; Risk Assessment ; Risk Factors ; Signal Transduction/drug effects ; Systems Integration
    Language English
    Publishing date 2012-01-10
    Publishing country United States
    Document type Letter
    ZDB-ID 2433157-0
    ISSN 1752-8062 ; 1752-8054
    ISSN (online) 1752-8062
    ISSN 1752-8054
    DOI 10.1111/j.1752-8062.2011.00367.x
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  5. Article ; Online: Exploring Biginelli-based scaffolds as A

    Prieto-Díaz, Rubén / Fojo-Carballo, Hugo / Majellaro, Maria / Tandarić, Tana / Azuaje, Jhonny / Brea, José / Loza, María I / Barbazán, Jorge / Salort, Glòria / Chotalia, Meera / Rodríguez-Pampín, Iván / Mallo-Abreu, Ana / Rita Paleo, M / García-Mera, Xerardo / Ciruela, Francisco / Gutiérrez-de-Terán, Hugo / Sotelo, Eddy

    Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

    2024  Volume 173, Page(s) 116345

    Abstract: Antagonists of the ... ...

    Abstract Antagonists of the A
    MeSH term(s) Humans ; Purinergic P1 Receptor Antagonists ; Receptor, Adenosine A2B/metabolism ; Adenosine A2 Receptor Antagonists/pharmacology ; Structure-Activity Relationship ; Antineoplastic Agents/pharmacology ; Colorectal Neoplasms/drug therapy
    Chemical Substances Purinergic P1 Receptor Antagonists ; Receptor, Adenosine A2B ; Adenosine A2 Receptor Antagonists ; Antineoplastic Agents
    Language English
    Publishing date 2024-03-05
    Publishing country France
    Document type Journal Article
    ZDB-ID 392415-4
    ISSN 1950-6007 ; 0753-3322 ; 0300-0893
    ISSN (online) 1950-6007
    ISSN 0753-3322 ; 0300-0893
    DOI 10.1016/j.biopha.2024.116345
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    Ud II Zs.198: Show issues Location:
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  6. Article: Gene Expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationships.

    Wang, Haiying / Azuaje, Francisco / Bodenreider, Olivier / Dopazo, Joaquín

    Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

    2015  Volume 2004, Page(s) 25–31

    Abstract: The Gene Ontology and annotations derived from ... ...

    Abstract The Gene Ontology and annotations derived from the
    Language English
    Publishing date 2015-02-05
    Publishing country United States
    Document type Journal Article
    DOI 10.1109/CIBCB.2004.1393927
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  7. Conference proceedings: DrDimont: Explainable drug response prediction from differential analysis of multi-omics networks

    Hiort, Pauline / Hugo, Julian / Zeinert, Justus / Müller, Nataniel / Kashyap, Spoorthi / Rajapakse, Jagath C. / Azuaje, Francisco / Renard, Bernhard Y. / Baum, Katharina

    2022  , Page(s) Abstr. 165

    Event/congress 67. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 13. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF); sine loco [digital]; Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; 2022
    Keywords Medizin, Gesundheit ; multi-omics ; molecular network ; drug prediction ; data integration ; explainable prediction
    Publishing date 2022-08-19
    Publisher German Medical Science GMS Publishing House; Düsseldorf
    Document type Conference proceedings
    DOI 10.3205/22gmds114
    Database German Medical Science

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  8. Article ; Online: Allergic airway inflammation delays glioblastoma progression and reinvigorates systemic and local immunity in mice.

    Poli, Aurélie / Oudin, Anaïs / Muller, Arnaud / Salvato, Ilaria / Scafidi, Andrea / Hunewald, Oliver / Domingues, Olivia / Nazarov, Petr V / Puard, Vincent / Baus, Virginie / Azuaje, Francisco / Dittmar, Gunnar / Zimmer, Jacques / Michel, Tatiana / Michelucci, Alessandro / Niclou, Simone P / Ollert, Markus

    Allergy

    2022  Volume 78, Issue 3, Page(s) 682–696

    Abstract: Background: Numerous patient-based studies have highlighted the protective role of immunoglobulin E-mediated allergic diseases on glioblastoma (GBM) susceptibility and prognosis. However, the mechanisms behind this observation remain elusive. Our ... ...

    Abstract Background: Numerous patient-based studies have highlighted the protective role of immunoglobulin E-mediated allergic diseases on glioblastoma (GBM) susceptibility and prognosis. However, the mechanisms behind this observation remain elusive. Our objective was to establish a preclinical model able to recapitulate this phenomenon and investigate the role of immunity underlying such protection.
    Methods: An immunocompetent mouse model of allergic airway inflammation (AAI) was initiated before intracranial implantation of mouse GBM cells (GL261). RAG1-KO mice served to assess tumor growth in a model deficient for adaptive immunity. Tumor development was monitored by MRI. Microglia were isolated for functional analyses and RNA-sequencing. Peripheral as well as tumor-associated immune cells were characterized by flow cytometry. The impact of allergy-related microglial genes on patient survival was analyzed by Cox regression using publicly available datasets.
    Results: We found that allergy establishment in mice delayed tumor engraftment in the brain and reduced tumor growth resulting in increased mouse survival. AAI induced a transcriptional reprogramming of microglia towards a pro-inflammatory-like state, uncovering a microglia gene signature, which correlated with limited local immunosuppression in glioma patients. AAI increased effector memory T-cells in the circulation as well as tumor-infiltrating CD4
    Conclusion: Our results demonstrate that AAI limits both tumor take and progression in mice, providing a preclinical model to study the impact of allergy on GBM susceptibility and prognosis, respectively. We identify a potentiation of local and adaptive systemic immunity, suggesting a reciprocal crosstalk that orchestrates allergy-induced immune protection against GBM.
    MeSH term(s) Mice ; Animals ; Glioblastoma/genetics ; Glioblastoma/pathology ; Brain Neoplasms/pathology ; Glioma/genetics ; Glioma/pathology ; Microglia/pathology ; Hypersensitivity/pathology ; Mice, Inbred C57BL
    Language English
    Publishing date 2022-10-18
    Publishing country Denmark
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 391933-x
    ISSN 1398-9995 ; 0105-4538
    ISSN (online) 1398-9995
    ISSN 0105-4538
    DOI 10.1111/all.15545
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    Zs.MO 125: Show issues

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  9. Article ; Online: Drug-target network in myocardial infarction reveals multiple side effects of unrelated drugs.

    Azuaje, Francisco J / Zhang, Lu / Devaux, Yvan / Wagner, Daniel R

    Scientific reports

    2011  Volume 1, Page(s) 52

    Abstract: The systems-level characterization of drug-target associations in myocardial infarction (MI) has not been reported to date. We report a computational approach that combines different sources of drug and protein interaction information to assemble the ... ...

    Abstract The systems-level characterization of drug-target associations in myocardial infarction (MI) has not been reported to date. We report a computational approach that combines different sources of drug and protein interaction information to assemble the myocardial infarction drug-target interactome network (My-DTome). My-DTome comprises approved and other drugs interlinked in a single, highly-connected network with modular organization. We show that approved and other drugs may both be highly connected and represent network bottlenecks. This highlights influential roles for such drugs on seemingly unrelated targets and pathways via direct and indirect interactions. My-DTome modules are associated with relevant molecular processes and pathways. We find evidence that these modules may be regulated by microRNAs with potential therapeutic roles in MI. Different drugs can jointly impact a module. We provide systemic insights into cardiovascular effects of non-cardiovascular drugs. My-DTome provides the basis for an alternative approach to investigate new targets and multidrug treatment in MI.
    MeSH term(s) Cardiotonic Agents/adverse effects ; Cardiotonic Agents/pharmacokinetics ; Cardiotonic Agents/therapeutic use ; Computer Simulation ; Drug-Related Side Effects and Adverse Reactions/chemically induced ; Drug-Related Side Effects and Adverse Reactions/metabolism ; Humans ; Models, Cardiovascular ; Myocardial Infarction/drug therapy ; Myocardial Infarction/metabolism
    Chemical Substances Cardiotonic Agents
    Language English
    Publishing date 2011-08-02
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/srep00052
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  10. Article: Ontology-driven approaches to analyzing data in functional genomics.

    Azuaje, Francisco / Al-Shahrour, Fatima / Dopazo, Joaquin

    Methods in molecular biology (Clifton, N.J.)

    2006  Volume 316, Page(s) 67–86

    Abstract: Ontologies are fundamental knowledge representations that provide not only standards for annotating and indexing biological information, but also the basis for implementing functional classification and interpretation models. This chapter discusses the ... ...

    Abstract Ontologies are fundamental knowledge representations that provide not only standards for annotating and indexing biological information, but also the basis for implementing functional classification and interpretation models. This chapter discusses the application of gene ontology (GO) for predictive tasks in functional genomics. It focuses on the problem of analyzing functional patterns associated with gene products. This chapter is divided into two main parts. The first part overviews GO and its applications for the development of functional classification models. The second part presents two methods for the characterization of genomic information using GO. It discusses methods for measuring functional similarity of gene products, and a tool for supporting gene expression clustering analysis and validation.
    MeSH term(s) Cluster Analysis ; Computational Biology/methods ; Data Interpretation, Statistical ; Gene Expression Profiling ; Genomics ; Humans
    Language English
    Publishing date 2006
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
    ISSN 1064-3745
    ISSN 1064-3745
    DOI 10.1385/1-59259-964-8:67
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