References of "Azuaje, Francisco"
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See detailA deep neural network approach to predicting clinical outcomes of neuroblastoma patients
Tranchevent, Leon-Charles UL; Azuaje, Francisco; Rajapakse, Jagath

in BMC Medical Genomics (2019), 12(8), 178

Background The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and ... [more ▼]

Background The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the “small n large p” problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. Methods We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients’ omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. Results We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Conclusions Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes. [less ▲]

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See detailDeconvolution of Transcriptomes and miRNomes by Independent Component Analysis Provides Insights Into Biological Processes and Clinical Outcomes of Melanoma Patients
Nazarov, Petr V.; Wienecke-Baldacchino, Anke K.; Zinovyev, Andrei et al

in BMC Medical Genomics (2019), 12 (1)(132),

Background: The amount of publicly available cancer-related“omics”data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis ... [more ▼]

Background: The amount of publicly available cancer-related“omics”data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Methods: Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA)–an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency. Results: By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible. Conclusions: We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival [less ▲]

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See detailStem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment
Dirkse, Anne; Golebiewska, Anna; Buder, Thomas et al

in Nature communications (2019), 10(1), 1787

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See detailSingle-cell transcriptomics reveals distinct inflammation-induced microglia signatures
Sousa, Carole UL; Golebiewska, Anna; Poovathingal, Suresh K et al

in EMBO Reports (2018)

Microglia are specialized parenchymal‐resident phagocytes of the central nervous system (CNS) that actively support, defend and modulate the neural environment. Dysfunctional microglial responses are ... [more ▼]

Microglia are specialized parenchymal‐resident phagocytes of the central nervous system (CNS) that actively support, defend and modulate the neural environment. Dysfunctional microglial responses are thought to worsen CNS diseases; nevertheless, their impact during neuroinflammatory processes remains largely obscure. Here, using a combination of single‐cell RNA sequencing and multicolour flow cytometry, we comprehensively profile microglia in the brain of lipopolysaccharide (LPS)‐injected mice. By excluding the contribution of other immune CNS‐resident and peripheral cells, we show that microglia isolated from LPS‐injected mice display a global downregulation of their homeostatic signature together with an upregulation of inflammatory genes. Notably, we identify distinct microglial activated profiles under inflammatory conditions, which greatly differ from neurodegenerative disease‐associated profiles. These results provide insights into microglial heterogeneity and establish a resource for the identification of specific phenotypes in CNS disorders, such as neuroinflammatory and neurodegenerative diseases. [less ▲]

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See detailAnalysis of the dynamic co-expression network of heart regeneration in the zebrafish
Rodius, Sophie; Androsova, Ganna UL; Götz, Lou et al

in Scientific Reports (2016), 6

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact ... [more ▼]

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration. [less ▲]

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See detailA comprehensive integrative analysis of the transcriptional network underlying the zebrafish heart regeneration
Androsova, Ganna UL; Rodius, Sophie; Nazarov, Petr et al

Poster (2014, September 08)

Despite a notable reduction in incidence of acute myocardial infarction (MI), patients who experienced it remain at risk for premature death and cardiac malfunction. The human cardiomyocytes are not able ... [more ▼]

Despite a notable reduction in incidence of acute myocardial infarction (MI), patients who experienced it remain at risk for premature death and cardiac malfunction. The human cardiomyocytes are not able to achieve extensive regeneration upon MI. Remarkably, the adult zebrafish is able to achieve complete heart regeneration following amputation, cryoinjury or genetic ablation. This raises new potential opportunities on how to boost heart healing capacity in humans. The objective of our research is to characterize the transcriptional network of the zebrafish heart regeneration and underlying regulatory mechanisms. To conduct our investigation, we used microarray data from zebrafish at 6 post-cryoinjury time points (4 hours, and 1, 3, 7, 14 and 90 days) and control samples. We thereon looked for the gene co-expression patterns in the data and, based on that, constructed a weighted gene co-expression network. To detect candidate functional sub-networks (modules), we used two different network clustering approaches: a density-based (ClusterONE) and a topological overlap-based (Hybrid Dynamic Branch Cut) algorithms. The visualization of the expression changes of the candidate modules reflected the dynamics of the recovery process. Also we aimed to identify candidate “hub” genes that might regulate the behavior of the biological modules and drive the regeneration process. We identified eighteen distinct modules associated with heart recovery upon cryoinjury. Functional enrichment analysis displayed that the modules are involved in different cellular processes crucial for heart regeneration, including: cell fate specification (p-value < 0.006) and migration (p-value < 0.047), ribosome biogenesis (p-value < 0.004), cardiac cell differentiation (p-value < 3E-04), and various signaling events (p-value < 0.037). The visualization of the modules’ expression profiles confirmed the relevance of these functional enrichments. For instance, the genes of the module involved in regulation of endodermal cell fate specification were up-regulated upon injury until 3 days. Among the candidate hub genes detected in the network, there are genes relevant to atherosclerosis treatment and inflammation during cardiac arrest. These and other findings are currently undergoing deeper computational analyses. The top promising targets will be independently validated using our zebrafish (in vivo) model. In conclusion, our findings provide insights into the complex regulatory mechanisms involved during heart regeneration in the zebrafish. These data will be useful for modelling specific network-based responses to heart injury, and for finding sensitive network points that may trigger or boost heart regeneration. [less ▲]

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