Browsing by Keyword "Microarray"
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Item Gene expression profiling of human gliomas reveals differences between GBM and LGA related to energy metabolism and notch signaling pathways(2007-05) Margareto, Javier; Leis, Olatz; Larrarte, Eider; Idoate, Miguel A.; Carrasco, Alejandro; Lafuente, José Vicente; Genética; GeneralesHuman malignant astrocytic tumors are the most common primary brain malignancies. Human gliomas are classified according to the extent of anaplasia or 'de-differentiation' appearance. Although this type of histological classification is widely accepted, the extensive heterogeneity of astrocytic tumors has made their pathological classification rather difficult. New genome-scale high throughput technologies for gene expression profiling, such as DNA microarrays, are emerging as new tools to allow a more accurate identification and characterization of different tumor degrees by discovering new specific markers and pathways of each stage. Present work reports interesting results that might be useful to differentiate between tumor grades. Data presented here provides new evidences about the molecular basis underlying different tumor stages. In this sense, we identified key metabolic pathways, crucial for tumor progression, as being differentially regulated in different tumor stages. On the other hand, remarkable findings regarding Notch pathway are reported, as some members of this receptor family were found to be differentially expressed depending on the malignancy degree. Our results clearly point out important molecular differences between different tumor stages and suggest that more studies are needed to understand specific molecular events characteristic of each stage. These types of studies represent a first step to deepen into the tumor physiology, which may potentially help for better and a more precise diagnosis of gliomas.Item Modelling and analysis of temporal gene expression data using spiking neural networks(Springer Verlag, 2018) Nandini, Durgesh; Capecci, Elisa; Koefoed, Lucien; Laña, Ibai; Shahi, Gautam Kishore; Kasabov, Nikola; Cheng, Long; Leung, Andrew Chi Sing; Ozawa, Seiichi; IA; Tecnalia Research & InnovationAnalysis of temporal gene expression data poses a significant challenge due to the combination of high dimensionality and low sample size. The purpose of this paper is to present a methodology for classification, modelling, and analysis of short time-series gene expression data using spiking neural networks (SNN) and to uncover temporal expression patterns for knowledge discovery. The classification is based on the NeuCube SNN model. Time-series gene expression data of mouse primary cortical neurons is examined as a case study. The results of the analysis are promising, indicating that SNN methodologies can be effectively used to model and analyse temporal gene expression data with surpassing performance over traditional machine learning algorithms. Additionally, a gene interaction network is constructed from the temporal gene activity modelled using the NeuCube architecture offering a new way of knowledge discovery. Future work will be directed towards using gene interactions networks to help guide pharmacological research for dementia.Item Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks(2020-12-01) Capecci, Elisa; Lobo, Jesus L.; Laña, Ibai; Espinosa-Ramos, Josafath I.; Kasabov, Nikola; IAThe genetic mechanisms responsible for the differentiation, metabolism, morphology and function of a cell in both normal and abnormal conditions can be uncovered by the analysis of transcriptomes. Mining big data such as the information encoded in nucleic acids, proteins, and metabolites has challenged researchers for several years now. Even though bioinformatics and system biology techniques have improved greatly and many improvements have been done in these fields of research, most of the processes that influence gene interaction over time are still unknown. In this study, we apply state-of-the art spiking neural network techniques to model, analyse and extract information about the regulatory processes of gene expression over time. A case study of microarray profiling in human skin during elicitation of eczema is used to examine the temporal association of genes involved in the inflammatory response, by means of a gene interaction network. Spiking neural network techniques are able to learn the interaction between genes using information encoded from the time-series gene expression data as spikes. The temporal interaction is learned, and the patterns of activity extracted and analysed with a gene interaction network. Results demonstrated that useful knowledge can be extracted from the data by using spiking neural network, unlocking some of the possible mechanisms involved in the regulatory process of gene expression.Item Profile of adipose tissue gene expression in premenopausal and postmenopausal women: Site-specific differences(2011-06) Gomez-Santos, Cecilia; Hernandez-Morante, Juan J.; Margareto, Javier; Larrarte, Eider; Formiguera, Xavier; Martínez, Carlos Manuel; Garaulet, Marta; Genética; GeneralesOBJECTIVES: Menopause increases the risk of several pathologies, probably due to enlarged levels of visceral fat. Apart from morphological and endocrine changes, a cluster of genes, still not fully defined, may be involved in these alterations. The objectives of the present study, therefore, were to analyze differences in adipose tissue gene expression between premenopausal and postmenopausal women and to ascertain whether any differences were depot specific. METHODS: Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) biopsies were taken from 7 premenopausal and 7 postmenopausal women undergoing surgery because of morbid obesity. RNA was extracted, and the overall gene expression profile was analyzed by microarray analysis. RESULTS: In general, SAT genes were overexpressed, whereas VAT genes were down-regulated in premenopausal compared with postmenopausal women. We found 724 differentially expressed genes in SAT and 327 in VAT. These differences suggest that several biological processes, such as the immune system and other metabolic processes, were altered based on menopause status. Regarding individual genes, neurexin 3, metallothionein 1E, and keratyn 7 showed the most pronounced differences. Interestingly, the expression of these genes was related to body fat distribution. CONCLUSIONS: Our results reveal that menopause influences the adipose tissue expression of many genes, especially of neurexin 3, metallothionein 1E, and keratyn 7, which are associated with the alteration of several key biological processes, such as the immune system and cell metabolism. Gene expression in adipose tissue could be used for diagnosis and the development of new therapeutic strategies against obesity and related alterations, depending on menopause status.