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Δημοσίευση Machine learning methods for genomic signature extraction(Technical University of Crete, 2015) Chlis Nikolaos-Kosmas; Χλης Νικολαος-Κοσμας; Zervakis Michalis; Ζερβακης Μιχαλης; Balas Costas; Μπαλας Κωστας; Mania Aikaterini; Μανια ΑικατερινηThe application of machine learning methodologies for the analysis of DNA microarray data has become a common practice in the field of bioinformatics. DNA microarrays can be used in order to simultaneously measure the expression value of thousands of genes. Given the measurements of gene expression, machine learning methods can be employed in order to identify candidate genes that are related to a biological state or phenotype of interest, such as cancer. These lists of candidate genes are often called “genomic signatures” in literature. The application of machine learning methods for the extraction of genomic signatures is a necessity, since it is practically impossible for field experts to assess the importance of each gene individually by manual inspection due to the large size of the genome, which consists of approximately 25,000 genes. Machine learning methods such as feature subset selection and classification algorithms are popular choices for the extraction of genomic signatures. Univariate feature selection methods filter genes according to difference in their gene expression profiles among samples belonging to different classes of interest, such as control and disease. Since they test each gene individually, univariate methods are computationally efficient and they select genes with high discrimination ability. However, they ignore associations among genes. On the other hand, multivariate methods simultaneously assess groups of genes and select candidate genes based on their predictive performance when used in conjunction with a classifier. As such, they are more efficient at capturing the latent associations among genes and select genes with high predictive capability, at the cost of being computationally expensive. While the applied feature selection and classification methodologies have matured and several state of the art algorithms have been established, the stability of the extracted genomic signatures is often overlooked. As a result, the genomic signatures extracted by many methodologies are unstable under sample variations. That is, the extracted signatures differ significantly under variations of the training data. Since result stability is related to generalization, this instability raises skepticism in the expert community and hinders the validity and clinical application of research findings extracted from such gene expression studies. This thesis deals with the following three aspects of the selection and evaluation of gene signatures, namely stability, predictive capability and statistical significance. First, a framework for the extraction of stable genomic signatures, called Stable Bootstrap Validation (SBV) is introduced. The proposed methodology enforces stability at the validation step. As a result, it can be combined with any classification method, as long as it supports feature selection. Three publicly available gene expression datasets are used in order to test the proposed methodology. First the dimensionality of the datasets is reduced using a filtering method. Then, bootstrap resampling is utilized in order to generate a list of candidate signatures according to the selection frequency of genes across all bootstrap datasets. Then, a stable signature which has maximal predictive performance in terms of accuracy, sensitivity and specificity is extracted and the predictive performance of all candidate signatures is plotted in an elaborate manner for further inspection. Additionally, the application of random sampling methods for countering the negative effects of imbalanced datasets in classification was investigated, since imbalanced datasets are frequently found in DNA microarray studies where control samples are usually scarce. Moreover, a proper statistical framework was implemented that includes two separate statistical tests, in order to assess the statistical significance of the extracted signature in terms of classification accuracy as well as association to the response variable (phenotype/biological state). Finally, the robustness of the methodology is assessed by testing the degree of “agreement” among signatures extracted from independent executions of the methodology.Δημοσίευση Non-linear synchronization methods on magnetoencephalographic (MEG) recordings(Technical University of Crete, 2015) Antonakakis Marios; Αντωνακακης Μαριος; Zervakis Michalis; Ζερβακης Μιχαλης; Lagoudakis Michael; Λαγουδακης Μιχαηλ; Mania Aikaterini; Μανια ΑικατερινηCross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. Furthermore, several neuroimaging studies have suggested that functional brain connectivity networks exhibit “small-world” characteristics, whereas recent studies based on structural data have proposed a “rich-club” organization of brain networks, whereby nodes of high connection density tend to connect among themselves compared to nodes of lower density. In this study, CFC profiles are analyzed from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. The non-linear synchronization metric, mutual information (MI) is used to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs (FCGs), a tensor representation and tensor subspace analysis is employed to identify an set of features with low dimensions for subject classification as mTBI or control. Keeping FCGs from the optimal set of features, an “attack strategy” to is developed to compare the rich-club and small-world organizations and identify the model that describes best the topology of brain connectivity. Results show that the controls form a dense network of stronger local and global connections, indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. Finally, the results suggest that resting state MEG connectivity networks follow a rich-club organization. These findings indicate that the analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI.