Machine Learning Model for Predicting CVD Risk on NHANES Data.

Cardiovascular disease (CVD) is a major health problem throughout the world. It is the leading cause of morbidity and mortality and also causes considerable economic burden to society. The early symptoms related to previous observations and abnormal events, which can be subjectively acquired by self-assessment of individuals, bear significant clinical relevance and are regularly preserved in the patient's health record. The aim of our study is to develop a machine learning model based on selected CVD-related information encompassed in NHANES data in order to assess CVD risk. This model can be used as a screening tool, as well as a retrospective reference in association with current clinical data in order to improve CVD assessment. In this form it is planned to be used for mass screening and evaluation of young adults entering their army service. The experimental results are promising in that the proposed model can effectively complement and support the CVD prediction for the timely alertness and control of cardiovascular problems aiming to prevent the occurrence of serious cardiac events.

Biclustering strategies for genetic marker selection in gynecologic tumor cell lines.

Over the past few decades great interest has been focused on cell lines derived from tumors, because of their usability as models to understand the biology of cancer. At the same time, advanced technologies such as DNA-microarrays have been broadly used to study the expression level of thousands of genes in primary tumors or cancer cell lines in a single experiment. Results from microarray analysis approaches have provided valuable insights into the underlying biology and proven useful for tumor classification, prognostication and prediction. Our approach utilizes biclustering methods for the discovery of genes with coherent expression across a subset of conditions (cell lines of a tumor type). More specifically, we present a novel modification on Cheng & Church's algorithm that searches for differences across the studied conditions, but also enforces consistent intensity characteristics of each cluster within each condition. The application of this approach on a gynecologic panel of cell lines succeeds to derive discriminant groups of compact bi-clusters across four types of tumor cell lines. In this form, the proposed approach is proven efficient for the derivation of tumor-specific markers.

Extracting reliable gene expression signatures through Stable Bootstrap Validation.

Identification of candidate genes responsible for specific phenotypes, such as cancer, has been a major challenge in the field of bioinformatics. Given a DNA Microarray dataset, traditional feature selection methods produce lists of candidate genes which vary significantly under variations of the training data. That instability hinders the validity of research findings and raises doubts about the reliability of such methods. In this study, we propose a framework for the extraction of stable genomic signatures. The proposed methodology enforces stability at the validation step, independent of the feature selection and classification methods used. The statistical significance of the selected gene set is also assessed. The results of this study demonstrate the importance of stability issues in genomic signatures, beyond their prediction capabilities.

Temporal and spatial patterns of gene profiles during chondrogenic differentiation.

Clustering analysis based on temporal profile of genes may provide new insights in particular biological processes or conditions. We report such an integrative clustering analysis which is based on the expression patterns but is also influenced by temporal changes. The proposed platform is illustrated with a temporal gene expression dataset comprised of pellet culture-conditioned human primary chondrocytes and human bone marrow-derived mesenchymal stem cells (MSCs). We derived three clusters in each cell type and compared the content of these classes in terms of temporal changes. We further considered the induced biological processes and the gene-interaction networks formed within each cluster and discuss their biological significance. Our proposed methodology provides a consistent tool that facilitates both the statistical and biological validation of temporal profiles through spatial gene network profiles.

Nonparametric network design and analysis of disease genes in oral cancer progression.

Biological networks in living organisms can be seen as the ultimate means of understanding the underlying mechanisms in complex diseases, such as oral cancer. During the last decade, many algorithms based on high-throughput genomic data have been developed to unravel the complexity of gene network construction and their progression in time. However, the small size of samples compared to the number of observed genes makes the inference of the network structure quite challenging. In this study, we propose a framework for constructing and analyzing gene networks from sparse experimental temporal data and investigate its potential in oral cancer. We use two network models based on partial correlations and kernel density estimation, in order to capture the genetic interactions. Using this network construction framework on real clinical data of the tissue and blood at different time stages, we identified common disease-related structures that may decipher the association between disease state and biological processes in oral cancer. Our study emphasizes an altered MET (hepatocyte growth factor receptor) network during oral cancer progression. In addition, we demonstrate that the functional changes of gene interactions during oral cancer progression might be particularly useful for patient categorization at the time of diagnosis and/or at follow-up periods.

Identification of altered MET network in oral cancer progression based on nonparametric network design.

Oral cancer is characterized by multiple genetic events such as alterations of a number of oncogenes and tumour suppressor genes. The aim of this study is to identify genes and their functional interactions that may play a crucial role on a specific disease-state, especially during oral cancer progression. We examine gene interaction networks on blood genomic data, obtained from twenty three oral cancer patients at four different time stages. We generate the gene-gene networks from sparse experimental temporal data using two methods, Partial Correlations and Kernel Density Estimation, in order to capture genetic interactions. The network study reveals an altered MET (hepatocyte growth factor receptor) network during oral cancer progression, which is further analyzed in relation to other studies.

Pattern of heat shock factor and heat shock protein expression in lymphocytes of bipolar patients: increased HSP70-glucocorticoid receptor heterocomplex.

Bipolar disorder (BD), a stress-related disease, is characterized by altered glucocorticoid receptor (GR) signalling. Stress response includes activation of heat shock factor (HSF) and subsequent heat shock protein (HSP) synthesis which regulate GR folding and function. The objective of this study was to investigate the possible role of HSFs, HSPs and their interaction with GR in BD. We applied immunoprecipitation, SDS-PAGE/Western blot analysis and electrophoretic mobility shift assay (EMSA) in lymphocytes (whole cell or nuclear extracts) from BD patients and healthy subjects and determined the HSPs (HSP90 and HSP70), the heterocomplexes HSP90-GR and HSP70-GR, the HSFs (HSF1 and HSF4) as well as the HSF-DNA binding. The HSP70-GR heterocomplex was elevated (p < 0.05) in BD patients vs healthy subjects, and nuclear HSP70 was reduced (p ≤ 0.01) in bipolar manic patients. Protein levels of HSF1, HSF4, HSP90, HSP90-GR heterocomplex, and HSF-DNA binding remained unaltered in BD patients vs healthy subjects. The corresponding effect sizes (ES) indicated a large ES for HSP70-GR, HSP70, HSF-DNA binding and HSF4, and a medium ES for HSP90, HSF1 and HSP90-GR between healthy subjects and bipolar patients. Significant correlations among HSFs, HSPs, GR and HSP70-GR heterocomplex were observed in healthy subjects, which were abrogated in bipolar patients. The higher interaction between GR and HSP70 and the disturbances in the relations among heat shock response parameters and GR as observed in our BD patients may provide novel insights into the contribution of these factors in BD aetiopathogenesis.