Outcome prediction based on microarray analysis: a critical perspective on methods

<p>Abstract</p> <p>Background</p> <p>Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation.</p> <p>Results</p> <p>A computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance.</p> <p>Conclusion</p> <p>Multiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.</p

FCLAB:An EEGLAB module for performing functional connectivity analysis on single-subject EEG data

Functional connectivity (FC) analysis constitutes a fundamental neuroscientific approach that has been extensively used for the investigation of brain's connectivity and activation patterns. To that end, several software tools have been developed. This paper presents FCLAB, the only EEGLAB-based plugin, which is able to work with EEG signals in order to estimate and visualize brain functional connectivity networks based on a variety of similarity measures as well as run a complete graph analysis procedure followed by a detailed visualization of the ensuing local and global measures distribution. FCLAB entails optimization procedures for the implementation of the connectivity structures and is the result of long-term research in EEG functional connectivity. The computed functional connectivity measures have been carefully selected to reflect the state-of-art in the field. Future work focuses on extending the platform for multi-subject analysis in order to enable the implementation of statistical analysis tools

Comparison of single trial back-projected independent components with the averaged waveform for the extraction of biomarkers of auditory P300 EPs

The independent components analysis (ICA) of the auditory P300 evoked responses in the EEG of normal subjects is described. The purpose was to identify any features which might provide the basis for biomarkers for diseases, such as Alzheimer’s disease. Single trial P300s were analysed by ICA, the activations were back-projected to scalp electrodes, many artefactual components were removed automatically, and the back-projected independent components (BICs) were first clustered according to their amplitudes and latencies. Then these primary clusters were secondarily clustered according to the columns of their mixing matrices, which clusters together those BICs with the same scalp topographies and, therefore, source locations. The BICs comprising the P300s had simple shapes, approximating half-sinusoids. Trial- to-trial variations in the BICs were found, which explain why different averages have been reported. Both positive- and also negative-going BICs were identified, some associated with known peaks in the P300 waveform. Artefact-free, single trial P300 waveforms could be constructed from the BICs, but these are probably of less interest than the BICs themselves. The findings demonstrate that neither averaged P300s, nor single trial P300s, are reliable as biomarkers, but rather it will be necessary to investigate the BICs present in a number of single trial realizations.peer-reviewe

Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender

During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks

Automatic Coastline Extraction Using Edge Detection and Optimization Procedures

Coastal areas are quite fragile landscapes as they are among the most vulnerable to climate change and natural hazards. Coastline mapping and change detection are essential for safe navigation, resource management, environmental protection, and sustainable coastal development and planning. In this paper, we proposed a new methodology for the automatic extraction of coastline, using aerial images. This method is based on edge detection and active contours (snake method). Initially the noise of the image is reduced which is followed by an image segmentation. The output images are further processed to remove all small spatial objects and to concentrate on the spatial objects of interests. Then, the morphological operators are applied. We used aerial images taken from an aircraft and high-resolution satellite images from a coastal area in Crete, Greece, and we compared the results with geodetic measurements, to validate the methodology

The linear neuron as marker selector and clinical predictor in cancer gene analysis

Summarization: The problem of gene selection has been extensively studied in a number of scientific works using various kinds of methods. However, the application of a linear neuron is a novel approach possessing several advantages. In this work we propose to study the behavior of such a linear neuron, appropriately adapted and trained to the problem of gene selection in the DNA microarray experiment.Παρουσιάστηκε στο: Computer methods and programs in biomedicin

Using a Single Neuron as a Marker Selector - A Breast Cancer Case Study

The problem of marker selection in DNA microarray analysis has been mostly addressed by linear methods. RFE-SVM is such a representative method where a linear kernel is used as the basic tool to address the problem. On the other hand a single neuron is known to be a linear estimator. In this study we explore such a single neuron to address the problem of marker selection. © 2007 IEEE

A proposal for gene Signature integration

Summarization: Gene expression patterns that can distinguish to a clinically significant degree disease subclasses not only play a prominent role in diagnosis but also lead to therapeutic strategies that tailor treatment to the particular biology of each disease. Nevertheless, gene expression signatures derived through statistical feature identification procedures on population datasets have received rightful criticism, since they share only few genes in common for a particular pathology, even if they derived from the same dataset using different methodologies. An optimistic view to this problem emerging from the wealth of biological interactions is that a statistical solution may not be unique. The derived signatures may be complementary parts of a global one, with each individual signature intersecting only a small part of biological evidence. In this work we focus on the biological knowledge hidden behind different gene signatures and propose a methodology for integrating such knowledge towards retrieving a unified signature.Παρουσιάστηκε στο: 9th International Conference on nformation Technology and Applications in Biomedicin