一种优化的生物数据多层关联规则挖掘算法

关联规则挖掘技术是寻找基因间关系的有效手段,但现有算法未针对高通量生物数据的特点进行优化,而存在着效率低下等缺点。提出的MAGO-FP算法,使用Gene Ontology(GO)的概念分层结构,通过对FP-Growth算法的扩展,具有一定的性能优势。在此基础上,应用该算法分析了一组由S.cerevisiae酵母菌cDNA微阵列芯片产生的实验数据,发现了一些候选关联规则。并针对其中一些重要的关联规则,通过相关文献证实了其真实性,表明该算法在基因表达分析等研究中具有应用价值。     

The adaptive classification of the dynamic spectral patterns in the human EEG]

The classification technique of single spectra based on a matrix of intercorrelation between these spectra and the fixed set of standard spectral patterns (SP) has been put forward. Including in the classification technique a special procedure for automatic adaptation of the standard SP to given EEG records makes it possible to reduce the number of unclassified single spectra to a minimum (6-10%), which we can ignore during comparative analysis of the EEG classification profiles. Using the universal set of standard SP makes it possible to compare the results of classification of different EEG records. The results of the analysis of classification profiles of human multichannel EEG during performance of the memory task on perception of visual images are described in the paper. It has been shown that both the total EEG reorganization associated with the alpha rhythm blockade during eyes opening and less noticeable EEG shifts accompanying changes in the stages of cognitive activity are underlain by a rather differentiated transformations of relative contributions of each type of the SP into the total power spectrum. It has been revealed that a relatively small part (15-20%) of the elementary EEG segments participates in the reorganization of the EEG classification profile.     

Measuring the dissimilarity between EEG recordings through a non-linear dynamical system approach

A new measure of dissimilarity between two EEG segments is proposed. It is derived from the application of the mathematical concept of distance between series of one-step predictions according to the estimated non-linear autoregressive functions. The non-linear autoregressive estimation is performed by non-parametric regression using kernel estimators. The possibility of applying this measure for automatic classification of EEG segments is explored. For this purpose multidimensional scaling and cluster analyses are applied on the basis of the calculated dissimilarity measures. In particular, its application to different EEG segments with delta activity and also with alpha waves reveals high agreement with visual classification by EEG specialists.     

Automatic detection of epileptic EEG signals using higher order cumulant features

The unpredictability of the occurrence of epileptic seizures makes it difficult to detect and treat this condition effectively. An automatic system that characterizes epileptic activities in EEG signals would allow patients or the people near them to take appropriate precautions, would allow clinicians to better manage the condition, and could provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect epileptic activity in EEG recordings. Because of the nonlinear and dynamic nature of EEG signals, the use of nonlinear Higher Order Spectra (HOS) features is a seemingly promising approach. This paper presents the methodology employed to extract HOS features (specifically, cumulants) from normal, interictal, and epileptic EEG segments and to use significant features in classifiers for the detection of these three classes. In this work, 300 sets of EEG data belonging to the three classes were used for feature extraction and classifier development and evaluation. The results show that the HOS based measures have unique ranges for the different classes with high confidence level (p-value < 0.0001). On evaluating several classifiers with the significant features, it was observed that the Support Vector Machine (SVM) presented a high detection accuracy of 98.5% thereby establishing the possibility of effective EEG segment classification using the proposed technique.     

Difficulty in clinical identification of neonatal seizures: an EEG monitor study

Seventeen newborns were monitored for 24 hours using a three-channel ambulatory EEG (A/EEG). All newborns were thought to be having subtle seizures by the nursery staff. Fifteen of the 17 newborns were recorded as having 1-30 clinical seizures during the time of monitoring. Only one newborn had clinically identified seizures associated with A/EEG discharges. The seizures were characterized by eye rolling. Fifty-two episodes (thought to be seizures) of lip smacking, bicycling, jerking, fisting, staring, stiffening, or any combination of the above occurred in eight newborns without an associated discharge on A/EEG. However, two of the eight had seizure discharges at other times, not associated with any clinical manifestation. Seventy-four apnea spells, thought to be possible seizures, occurred in seven newborns. None was associated with discharges on A/EEG, but one of these newborns had 50 A/EEG discharges unrelated to apnea or other clinical manifestations.     

A framework for direct locating and conformational sampling of protein structural motifs

A specific treatment of recurrent structural motifs that represent the local bias information has been proven to be an important ingredient in de novo protein structure predication. Significant majority of methods for local structure are based on building blocks, which still suffer from its inherent discrete nature. Instead of using building blocks, this work presents a new protocol framework for local structural motifs prediction based on the direct locating along protein sequence and probabilistic sampling in a continuous (φ, ψ) space. The protein sequence was first scanned by an algorithm of sliding window with variable length of 7 to 19 residues, to match local segments to one of 82 motifs patterns in the fragment library. Identified segments were then labeled and modeled as the correlations of backbone torsion angles with mixture of bivariate cosine distributions in continuous (φ, ψ) space. 3D conformations of corresponding segments were finally sampled by using a backtrack algorithm to the hidden Markov model with single output of (φ, ψ). For local motifs in 50 proteins of testing set, about 62% of eight-residue segments located with high confidence value were predicted within 1.5 ? of their native structures by the method. Majority of local structural motifs were identified and sampled, which indicates the proposed protocol may at least serve as the foundation to obtain better protein tertiary structure prediction.     

Automated EEG preprocessing during anaesthesia: new aspects using artificial neural networks.

The computer-aided detection of artefacts became an essential task with increasing automation of quantitative electroencephalogram (EEG) analysis during anaesthesiological applications. The different algorithms published so far required individual manual adjustment or have been based on limited decision criteria. In this study, we developed an artificial neural networks-(ANN-)aided method for automated detection of artefacts and EEG suppression periods. 72 hr EEG recorded before, during and after anaesthesia with propofol have been evaluated. Selected parameterized patterns of 0.25 s length were used to train the ANN (22 input, 8 hidden and 4 output neurons) with error back propagation. The detection performance of the ANN-aided method was tested with processing epochs between 1 to10 s. Related to examiner EEG evaluation, the average detection performance of the method was 72% sensitivity and 80% specificity for artefacts and 90% sensitivity and 92% specificity for EEG suppression. The improvement in signal-to-noise ratio with automated artefact processing was 1.39 times for the spectral edge frequency 95 (SEF95) and 1.89 times for the approximate entropy (ApEn). We conclude that ANN-aided preprocessing provide an useful tool for automated EEG evaluation in anaesthesiological applications.     

Efficient fitting of conductance-based model neurons from somatic current clamp

Estimating biologically realistic model neurons from electrophysiological data is a key issue in neuroscience that is central to understanding neuronal function and network behavior. However, directly fitting detailed Hodgkin?CHuxley type model neurons to somatic membrane potential data is a notoriously difficult optimization problem that can require hours/days of supercomputing time. Here we extend an efficient technique that indirectly matches neuronal currents derived from somatic membrane potential data to two-compartment model neurons with passive dendrites. In consequence, this approach can fit semi-realistic detailed model neurons in a few minutes. For validation, fits are obtained to model-derived data for various thalamo-cortical neuron types, including fast/regular spiking and bursting neurons. A key aspect of the validation is sensitivity testing to perturbations arising in experimental data, including sampling rates, inadequately estimated membrane dynamics/channel kinetics and intrinsic noise. We find that maximal conductance estimates and the resulting membrane potential fits diverge smoothly and monotonically from near-perfect matches when unperturbed. Curiously, some perturbations have little effect on the error because they are compensated by the fitted maximal conductances. Therefore, the extended current-based technique applies well under moderately inaccurate model assumptions, as required for application to experimental data. Furthermore, the accompanying perturbation analysis gives insights into neuronal homeostasis, whereby tuning intrinsic neuronal properties can compensate changes from development or neurodegeneration.     

Analysis and automatic identification of sleep stages using higher order spectra

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.     

Learning to represent visual input

One of the central problems in computational neuroscience is to understand how the object-recognition pathway of the cortex learns a deep hierarchy of nonlinear feature detectors. Recent progress in machine learning shows that it is possible to learn deep hierarchies without requiring any labelled data. The feature detectors are learned one layer at a time and the goal of the learning procedure is to form a good generative model of images, not to predict the class of each image. The learning procedure only requires the pairwise correlations between the activations of neuron-like processing units in adjacent layers. The original version of the learning procedure is derived from a quadratic ‘energy’ function but it can be extended to allow third-order, multiplicative interactions in which neurons gate the pairwise interactions between other neurons. A technique for factoring the third-order interactions leads to a learning module that again has a simple learning rule based on pairwise correlations. This module looks remarkably like modules that have been proposed by both biologists trying to explain the responses of neurons and engineers trying to create systems that can recognize objects.     

Explicit-duration hidden Markov model inference of UP-DOWN states from continuous signals

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics.     

Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks

A hybrid system (hidden neural network) based on a hidden Markov model (HMM) and neural networks (NN) was trained to predict the bonding states of cysteines in proteins starting from the residue chains. Training was performed using 4136 cysteine-containing segments extracted from 969 non-homologous proteins of well-resolved 3D structure and without chain-breaks. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 80% using neural networks based on evolutionary information. When the whole protein is taken into account by means of an HMM, a hybrid system is generated, whose emission probabilities are computed using the NN output (hidden neural networks). In this case, the predictor accuracy increases up to 88%. Further, when tested on a protein basis, the hybrid system can correctly predict 84% of the chains in the data set, with a gain of at least 27% over the NN predictor.     

A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data

Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing.     

A single spiking neuron that can represent interval timing: analysis,plasticity and multi-stability

The ability to represent interval timing is crucial for many common behaviors, such as knowing whether to stop when the light turns from green to yellow. Neural representations of interval timing have been reported in the rat primary visual cortex and we have previously presented a computational framework describing how they can be learned by a network of neurons. Recent experimental and theoretical results in entorhinal cortex have shown that single neurons can exhibit persistent activity, previously thought to be generated by a network of neurons. Motivated by these single neuron results, we propose a single spiking neuron model that can learn to compute and represent interval timing. We show that a simple model, reduced analytically to a single dynamical equation, captures the average behavior of the complete high dimensional spiking model very well. Variants of this model can be used to produce bi-stable or multi-stable persistent activity. We also propose a plasticity rule by which this model can learn to represent different intervals and different levels of persistent activity.     

Computation of the electroencephalogram (EEG) from network models of point neurons

The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent’s EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85–95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2–8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings.     

Growth patterns of neuronal dendrites — an attempted probabilistic description

A mathematical model of dendritic branching patterns of cortical neurons is presented which describes the ontogenetic development as a function of space and time. Comparison with experimental data from the literature suggests strongly that the growth rate of a dendrite fluctuates around a mean value; the mean value is probably age-dependent. Furthermore, at the site of a bifurcation a variable delay in outgrowth for one of the two daughter segments is conjectured.     

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.     

Organization of hindbrain segments in the zebrafish embryo

To learn how neural segments are structured in a simple vertebrate, we have characterized the embryonic zebrafish hindbrain with a library of monoclonal antibodies. Two regions repeat in an alternating pattern along a series of seven segments. One, the neuromere centers, contains the first basal plate neurons to develop and the first neuropil. The other region, surrounding the segment boundaries, contains the first neurons to develop in the alar plate. The projection patterns of these neurons differ: those in the segment centers have descending axons, while those in the border regions form ventral commissures. A row of glial fiber bundles forms a curtain-like structure between each center and border region. Specific features of the individual hindbrain segments in the series arise within this general framework. We suggest that a cryptic simplicity underlies the eventual complex structure that develops from this region of the CNS.     

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.