Ioannis Valavanis

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context.

BackgroundObesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.ResultsPDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.ConclusionsThe ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.

A comparative study of multi-classification methods for protein fold recognition

Fold recognition based on sequence-derived features is a complex multi-class classification problem. In the current study, we comparatively assess five different classification techniques, namely multilayer perceptron and probabilistic neural networks, nearest neighbour classifiers, multi-class support vector machines and classification trees for fold recognition on a reference set of proteins that are organised in 27 folds and are described by 125-dimensional vectors of sequence-derived features. We evaluate all classifiers in terms of total accuracy, mutual information coefficient, sensitivity and specificity measurements using a ten-fold cross-validation method. A polynomial support vector machine and a multilayer perceptron of one hidden layer of 88 nodes performed better and achieved satisfactory multi-class classification accuracies (42.8% and 42.1%, respectively) given the complexity of the problem and the reported similar classification performances of other researchers.

Omics for prediction of environmental health effects : Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.

Assessment of carotid atherosclerosis from B-mode ultrasound images using directional multiscale texture features

In this paper, three multiscale transforms with directional character, namely the dual-tree complex wavelet (DTCWT), the finite ridgelet (FRIT) and the fast discrete curvelet (FDCT) transforms, were comparatively assessed with respect to their ability to characterize carotid atherosclerotic plaque from B-mode ultrasound and discriminate between symptomatic and asymptomatic cases. The standard deviation and entropy of the detail subimages produced for each decomposition scheme were used as texture features. Feature selection included ranking the features according to their highest separability value and the minimum correlation among them. Due to the rather limited size of the sample population, the selected features were resampled 100 times by the bootstrap technique and divided into training and test sets. For each pair of sets, a support vector machine classifier was trained on the training set and evaluated on the test set. The average overall classification performance for systole (diastole) was 70% (65.2%), 72.6% (70.4%) and 84.9% (73.6%) for the DTCWT, FRIT and FDCT, respectively. These preliminary results showed the superiority of the curvelet transform, in terms of classification accuracy, being of great importance for the diagnosis and management of plaque instability in carotid atheromatous stenosis.

DIAGNOSIS: A Telematics Enabled System for Medical Image Archiving, Management and Diagnosis Assistance

In this paper, a modular system for medical image archiving, management, diagnosis support, and telematic cooper- ation is presented. The system provides digital imaging and com- munications in medicine (DICOM)-compatible tools for digital image processing and database management of medical images. The software features algorithms for preprocessing, manual or semi-automatic segmentation, automatic calculation of geomet- rical/size characteristics, and 3-D visualization of organs or se- lected regions of interest. Additionally, the system incorporates a database where patient data and information can be stored and retrieved. Access to the database is only permitted to authorized users. The user-friendly interface makes the software handy and accessible to clinicians, whereas the telematic components allow collaboration with remote experts. The pilot system incorporates a computer-aided diagnosis module aiming at providing support in the diagnosis of focal liver lesions from computed tomography images.

Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases

We recently reported that differential gene expression and DNA methylation profiles in blood leukocytes of apparently healthy smokers predicts with remarkable efficiency diseases and conditions known to be causally associated with smoking, suggesting that blood-based omic profiling of human populations may be useful for linking environmental exposures to potential health effects. Here we report on the sex-specific effects of tobacco smoking on transcriptomic and epigenetic features derived from genome-wide profiling in white blood cells, identifying 26 expression probes and 92 CpG sites, almost all of which are affected only in female smokers. Strikingly, these features relate to numerous genes with a key role in the pathogenesis of cardiovascular disease, especially thrombin signaling, including the thrombin receptors on platelets F2R (coagulation factor II (thrombin) receptor; PAR1) and GP5 (glycoprotein 5), as well as HMOX1 (haem oxygenase 1) and BCL2L1 (BCL2-like 1) which are involved in protection against oxidative stress and apoptosis, respectively. These results are in concordance with epidemiological evidence of higher female susceptibility to tobacco-induced cardiovascular disease and underline the potential of blood-based omic profiling in hazard and risk assessment.

Exploring Robust Diagnostic Signatures for Cutaneous Melanoma Utilizing Genetic and Imaging Data

Multimodal data combined in an integrated dataset can be used to aim the identification of instrumental biological actions that trigger the development of a disease. In this paper, we use an integrated dataset related to cutaneous melanoma that fuses two separate sets providing complementary information (gene expression profiling and imaging). Our first goal is to select a subset of genes that comprise candidate genetic biomarkers. The derived gene signature is then utilized in order to select imaging features, which characterize disease at a macroscopic level, presenting the highest, mutual information content to the selected genes. Using information gain ratio measurements and exploration of the gene ontology tree, we identified a set of 32 uncorrelated genes with a pivotal role as regards molecular regulation of melanoma, which expression across samples correlates highly with the different pathological states. These genes steered the selection of a subset of uncorrelated imaging features based on their ranking according to mutual information measurements to the selected gene expression values. Selected genes and imaging features were used to train various classifiers that could generalize well when discriminating malignant from benign melanoma samples. Results on the selection on imaging features and classification were compared to feature selection based on a straight forward statistical selection and a stochastic-based methodology. Genes in the backstage of low-level biological processes showed to carry higher information content than the macroscopic imaging features.

A Composite Framework for the Statistical Analysis of Epidemiological DNA Methylation Data with the Infinium Human Methylation 450K BeadChip

High-throughput DNA methylation profiling exploits microarray technologies thus providing a wealth of data, which however solicits rigorous, generic, and analytical pipelines for an efficient systems level analysis and interpretation. In this study, we utilize the Illuminas Infinium Human Methylation 450K BeadChip platform in an epidemiological cohort, targeting to associate interesting methylation patterns with breast cancer predisposition. The computational framework proposed here extends the-established in transcriptomic microarrays-logarithmic ratio of the methylated versus the unmethylated signal intensities, quoted as M -value. Moreover, intensity-based correction of the M-signal distribution is introduced in order to correct for batch effects and probe-specific errors in intensity measurements. This is accomplished through the estimation of intensity-related error measures from quality control samples included in each chip. Moreover, robust statistical measures exploiting the coefficient variation of DNA methylation measurements between control and case samples alleviate the impact of technical variation. The results presented here are juxtaposed to those derived by applying classical preprocessing and statistical selection methodologies. Overall, in comparison to traditional approaches, the superior performance of the proposed framework in terms of technical bias correction, along with its generic character, support its suitability for various microarray technologies.

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods

DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies.

Analyzing and Visualizing Genomic Complexity for the Derivation of the Emergent Molecular Networks

Modern genomic studies, accumulation of biological information in repositories, plus novel analytical and data-mining methodologies, comprise the backbone for the holistic explanation of intricate phenotypes, interrogated by high-throughput experiments. Recent developments in web platforms architecture, in conjunction with novel, browser-centric, visualization techniques pose a powerful framework for the development of distributed web applications, which execute complex analytical tasks, display the results in user-friendly interface and produce comprehensive, visualization charts. In this paper, the presented client-server application targets the systemic interpretation of input gene lists, through the fusion of established statistical methodologies and information-mining techniques, while interactive visualization modules aid the intuitive interpretation of results. Two publicly available datasets, related to Crohns and Parkinsons disease are used to present application analytical efficiency, robustness and functionalities.