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  • NARCIS (Royal Netherlands Academy of Arts and Sciences)  (4)
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  • 1
    In: PLoS ONE, 2018, Vol.13(2)
    Description: Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.
    Keywords: Research Article ; Research And Analysis Methods ; Physical Sciences ; Computer And Information Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Medicine And Health Sciences
    E-ISSN: 1932-6203
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  • 2
    In: PLoS ONE, 2017, Vol.12(8)
    Description: Objectives Glycaemic control in children and adolescents with type 1 diabetes mellitus can be challenging, complex and influenced by many factors. This study aimed to identify patient characteristics that were predictive of satisfactory glycaemic control in the paediatric population using a logistic regression mixed-effects (population) modelling approach. Methods The data were obtained from 288 patients aged between 1 and 22 years old recorded retrospectively over 3 years (1852 HbA1c observations). HbA1c status was categorised as ‘satisfactory’ or ‘unsatisfactory’ glycaemic control, using an a priori cut-off value of HbA1c ≥ 9% (75 mmol/mol), as used routinely by the hospital’s endocrine paediatricians. Patients’ characteristics were tested as covariates in the model as potential predictors of glycaemic control. Results There were three patient characteristics identified as having a significant influence on glycaemic control: HbA1c measurement at the beginning of the observation period (Odds Ratio (OR) = 0.30 per 1% HbA1c increase, 95% confidence interval (CI) = 0.20–0.41); Age (OR = 0.88 per year increase, 95% CI = 0.80–0.94), and fractional disease duration (disease duration/age, OR = 0.80 per 0.10 increase, 95% CI = 0.66–0.93) were collectively identified as factors contributing significantly to lower the probability of satisfactory glycaemic control. Conclusions The study outcomes may prove useful for identifying paediatric patients at risk of having unsatisfactory glycaemic control, and who could require more extensive monitoring, support, or targeted interventions.
    Keywords: Research Article ; Medicine And Health Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Medicine And Health Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; People And Places ; Medicine And Health Sciences ; Physical Sciences ; Medicine And Health Sciences
    E-ISSN: 1932-6203
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  • 3
    In: Experimental & Molecular Medicine, 2018, Vol.50(3), p.e453
    Description: New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.
    Keywords: Biomedical Research ; Systems Analysis;
    ISSN: 12263613
    E-ISSN: 2092-6413
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  • 4
    Language: English
    In: Molecular & Cellular Proteomics, 2019, Vol.18(5), pp.892-908
    Description: Staphylococcus aureus is infamous for causing recurrent infections of the human respiratory tract. This is a consequence of its ability to adapt to different niches, including the intracellular milieu of lung epithelial cells. To understand the dynamic...
    Keywords: Apoptosis* ; Autophagy ; Bacteria ; Energy Metabolism ; Host-Pathogen Interaction ; Infectious Disease ; Staphylococcus Aureus ; Bronchial Epithelial Cells ; in Vivo Proteomics ; Persister ; Population Heterogeneity;
    ISSN: 1535-9476
    E-ISSN: 15359484
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