Search tips
Search criteria

Results 1-25 (933549)

Clipboard (0)

Related Articles

1.  The Use of Neural Network Technology to Model Swimming Performance 
The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports.
Key pointsThe non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance).
PMCID: PMC3778687  PMID: 24149233
Evaluation; age group swimmers; individual medley; front crawl.
2.  Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations 
Clinics  2010;65(12):1223-1228.
To evaluate the performance of support vector machine, multi‐layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps.
A total of 318 maps were selected and classified into four categories: normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive keratectomy (n = 11). For each map, 11 attributes were obtained or calculated from data provided by the Orbscan II. Ten‐fold cross‐validation was used to train and test the classifiers. Besides accuracy, sensitivity and specificity, receiver operating characteristic (ROC) curves for each classifier were generated, and the areas under the curves were calculated.
The three selected classifiers provided a good performance, and there were no differences between their performances. The area under the ROC curve of the support vector machine, multi‐layer perceptron and radial basis function neural network were significantly larger than those for all individual Orbscan II attributes evaluated (p<0.05).
Overall, the results suggest that using a support vector machine, multi‐layer perceptron classifiers and radial basis function neural network, these classifiers, trained on Orbscan II data, could represent useful techniques for keratoconus detection.
PMCID: PMC3020330  PMID: 21340208
Neural networks; Artificial intelligence; Clinical decision support systems; Corneal topography; Diagnosis
3.  An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network 
Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classification errors in the results of image segmentation.
Objective: An automated algorithm based on multi-layer perceptron neural networks (MLPNN) is presented for segmenting MR images. The system is to identify two tissues of WM and GM in human brain 2D structural MR images. A given 2D image is processed to enhance image intensity and to remove extra cerebral tissue. Thereafter, each pixel of the image under study is represented using 13 features (8 statistical and 5 non- statistical features) and is classified using a MLPNN into one of the three classes WM and GM or unknown.
Results: The developed MR image segmentation algorithm was evaluated using 20 real images. Training using only one image, the system showed robust performance when tested using the remaining 19 images. The average Jaccard similarity index and Dice similarity metric for the GM and WM tissues were estimated to be 75.7 %, 86.0% for GM, and 67.8% and 80.7%for WM, respectively.
Conclusion: The obtained performances are encouraging and show that the presented method may assist with segmentation of 2D MR images especially where categorizing WM and GM is of interest.
PMCID: PMC4204503  PMID: 25505757
Image segmentation; Artificial neural networks; Multi-layer perceptron
4.  Skin lesion classification using relative color features 
Clinically, it is difficult to differentiate the early stage of malignant melanoma and certain benign skin lesions due to similarity in appearance. Our research uses image analysis of clinical skin images and relative color-based pattern recognition techniques to enhance the images and improve differentiation of these lesions.
First, the relative color images were created from digitized photographic images. Then, they were segmented into objects and morphologically filtered. Next, the relative color features were extracted from the objects to form two different feature spaces; a lesion feature space and an object feature space. The two feature spaces serve as two data models to be analyzed to determine the best features. Finally, we used a statistical analysis model of relative color features, which better classifies the various types of skin lesions.
The best features found for differentiation of melanoma and benign skin lesions from this study are area, mean in the red and blue bands, standard deviation in the red and green bands, skewness in the green band, and entropy in the red band. With the relative color feature algorithm developed, the best results were obtained with a multi-layer perceptron neural network model. This showed an overall classification success of 79%, with 70% of the benign lesions successfully classified, and 86% of malignant melanoma successfully classified.
PMCID: PMC3184884  PMID: 18211602
skin lesion; melanoma; relative color features; image classification; pattern recognition; CVIPtools
5.  Effects of BDNF Polymorphisms on Antidepressant Action 
Psychiatry Investigation  2010;7(4):236-242.
Evidence suggests that the down-regulation of the signaling pathway involving brain-derived neurotrophic factor (BDNF), a molecular element known to regulate neuronal plasticity and survival, plays an important role in the pathogenesis of major depression. The restoration of BDNF activity induced by antidepressant treatment has been implicated in the antidepressant therapeutic mechanism. Because there is variability among patients with major depressive disorder in terms of response to antidepressant treatment and since genetic factors may contribute to this inter-individual variability in antidepressant response, pharmacogenetic studies have tested the associations between genetic polymorphisms in candidate genes related to antidepressant therapeutic action. In human BDNF gene, there is a common functional polymorphism (Val66Met) in the pro-region of BDNF, which affects the intracellular trafficking of proBDNF. Because of the potentially important role of BDNF in the antidepressant mechanism, many pharmacogenetic studies have tested the association between this polymorphism and the antidepressant therapeutic response, but they have produced inconsistent results. A recent meta-analysis of eight studies, which included data from 1,115 subjects, suggested that the Val/Met carriers have increased antidepressant response in comparison to Val/Val homozygotes, particularly in the Asian population. The positive molecular heterosis effect (subjects heterozygous for a specific genetic polymorphism show a significantly greater effect) is compatible with animal studies showing that, although BDNF exerts an antidepressant effect, too much BDNF may have a detrimental effect on mood. Several recommendations are proposed for future antidepressant pharmacogenetic studies of BDNF, including the consideration of multiple polymorphisms and a haplotype approach, gene-gene interaction, a single antidepressant regimen, controlling for age and gender interactions, and pharmacogenetic effects on specific depressive symptom-clusters.
PMCID: PMC3022309  PMID: 21253406
Major depression; Antidepressant; Brain-derived neurotrophic factor; Polymorphisms
6.  Bacteria classification using Cyranose 320 electronic nose 
An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds.
Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got 74% classification accuracy from PCA. An innovative data clustering approach was investigated for these bacteria data by combining the 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes.
A [6 × 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict six classes of bacteria with up to 98% accuracy with the application of the RBF network.
This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320.
PMCID: PMC149373  PMID: 12437783
7.  Colon cancer prediction with genetic profiles using intelligent techniques 
Bioinformation  2008;3(3):130-133.
Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t‐statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t‐statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques.
PMCID: PMC2639687  PMID: 19238250
gene expression; tumor classification; t-statistic; feature selection; SVM neural network; logistic regression
8.  Comparison of Statistical Methods for Identification of Streptococcus thermophilus, Enterococcus faecalis, and Enterococcus faecium from Randomly Amplified Polymorphic DNA Patterns 
Thermophilic streptococci play an important role in the manufacture of many European cheeses, and a rapid and reliable method for their identification is needed. Randomly amplified polymorphic DNA (RAPD) PCR (RAPD-PCR) with two different primers coupled to hierarchical cluster analysis has proven to be a powerful tool for the classification and typing of Streptococcus thermophilus, Enterococcus faecium, and Enterococcus faecalis (G. Moschetti, G. Blaiotta, M. Aponte, P. Catzeddu, F. Villani, P. Deiana, and S. Coppola, J. Appl. Microbiol. 85:25–36, 1998). In order to develop a fast and inexpensive method for the identification of thermophilic streptococci, RAPD-PCR patterns were generated with a single primer (XD9), and the results were analyzed using artificial neural networks (Multilayer Perceptron, Radial Basis Function network, and Bayesian network) and multivariate statistical techniques (cluster analysis, linear discriminant analysis, and classification trees). Cluster analysis allowed the identification of S. thermophilus but not of enterococci. A Bayesian network proved to be more effective than a Multilayer Perceptron or a Radial Basis Function network for the identification of S. thermophilus, E. faecium, and E. faecalis using simplified RAPD-PCR patterns (obtained by summing the bands in selected areas of the patterns). The Bayesian network also significantly outperformed two multivariate statistical techniques (linear discriminant analysis and classification trees) and proved to be less sensitive to the size of the training set and more robust in the response to patterns belonging to unknown species.
PMCID: PMC92850  PMID: 11319095
9.  Resting State Network Estimation in Individual Subjects 
NeuroImage  2013;82:616-633.
Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative.
PMCID: PMC3909699  PMID: 23735260
fMRI; Resting State Network; Multilayer Perceptron; Functional Connectivity; Supervised Classifier; Brain Mapping
10.  Identification of ribosome binding sites in Escherichia coli using neural network models. 
Nucleic Acids Research  1995;23(9):1632-1639.
This study investigated the use of neural networks in the identification of Escherichia coli ribosome binding sites. The recognition of these sites based on primary sequence data is difficult due to the multiple determinants that define them. Additionally, secondary structure plays a significant role in the determination of the site and this information is difficult to include in the models. Efforts to solve this problem have so far yielded poor results. A new compilation of E. coli ribosome binding sites was generated for this study. Feedforward backpropagation networks were applied to their identification. Perceptrons were also applied, since they have been the previous best method since 1982. Evaluation of performance for all the neural networks and perceptrons was determined by ROC analysis. The neural network provided significant improvement in the recognition of these sites when compared with the previous best method, finding less than half the number of false positives when both models were adjusted to find an equal number of actual sites. The best neural network used an input window of 101 nucleotides and a single hidden layer of 9 units. Both the neural network and the perceptron trained on the new compilation performed better than the original perceptron published by Stormo et al. in 1982.
PMCID: PMC306908  PMID: 7784221
11.  Exploration of Computational Methods for Classification of Movement Intention during Human Voluntary Movement from Single Trial EEG 
To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).
Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed.
The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention.
Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training.
Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.
PMCID: PMC4154235  PMID: 17967559
Movement intention; Self-paced movement; Combination; Computational methods; Classification; Movement-related cortical potentials (MRCPs); Event-related desynchronization/synchronization (ERD/ERS); Genetic Algorithm; Brain-computer interface (BCI)
12.  Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling 
Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such responses. The classification method targeted signals containing error-related negativity (ERN) and error positivity (Pe) components, which are typically associated with error processing in the human brain. Feature extraction consisted of Multivariate Autoregressive modeling combined with the Simulated Annealing technique. The resulting information was subsequently classified by means of an Artificial Neural Network (ANN) using back-propagation algorithm under the “leave-one-out cross-validation” scenario and the Fuzzy C-Means (FCM) algorithm. The ANN consisted of a multi-layer perceptron (MLP). The approach yielded classification rates of up to 85%, both for the actors’ correct and incorrect responses and the corresponding ERPs of the observers. The electrodes needed for such classifications were situated mainly at central and frontal areas. Results provide indications that the classification of the ERN is achievable. Furthermore, the availability of the Pe signals, in addition to the ERN, improves the classification, and this is more pronounced for observers’ signals. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in performance monitoring and joint-action research, in both healthy and patient populations.
PMCID: PMC2705112  PMID: 19587809
13.  Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data 
Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information.
Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome.
Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings.
This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results respect to the SFS approach, independently of the classifier used. Regarding classifiers, even if C-MANTEC did not achieve the best overall results, the performance was competitive with a very robust behaviour in terms of the parameters of the algorithm, and thus it can be considered as a candidate technique for future studies.
PMCID: PMC4108856  PMID: 25077572
Microarray; Genetic algorithms; Constructive neural networks; Feature Selection
14.  Financial Time Series Prediction Using Spiking Neural Networks 
PLoS ONE  2014;9(8):e103656.
In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.
PMCID: PMC4149346  PMID: 25170618
15.  Heuristic modeling of macromolecule release from PLGA microspheres 
Dissolution of protein macromolecules from poly(lactic-co-glycolic acid) (PLGA) particles is a complex process and still not fully understood. As such, there are difficulties in obtaining a predictive model that could be of fundamental significance in design, development, and optimization for medical applications and toxicity evaluation of PLGA-based multiparticulate dosage form. In the present study, two models with comparable goodness of fit were proposed for the prediction of the macromolecule dissolution profile from PLGA micro- and nanoparticles. In both cases, heuristic techniques, such as artificial neural networks (ANNs), feature selection, and genetic programming were employed. Feature selection provided by fscaret package and sensitivity analysis performed by ANNs reduced the original input vector from a total of 300 input variables to 21, 17, 16, and eleven; to achieve a better insight into generalization error, two cut-off points for every method was proposed. The best ANNs model results were obtained by monotone multi-layer perceptron neural network (MON-MLP) networks with a root-mean-square error (RMSE) of 15.4, and the input vector consisted of eleven inputs. The complicated classical equation derived from a database consisting of 17 inputs was able to yield a better generalization error (RMSE) of 14.3. The equation was characterized by four parameters, thus feasible (applicable) to standard nonlinear regression techniques. Heuristic modeling led to the ANN model describing macromolecules release profiles from PLGA microspheres with good predictive efficiency. Moreover genetic programming technique resulted in classical equation with comparable predictability to the ANN model.
Video abstract
PMCID: PMC3857266  PMID: 24348037
poly(lactic-co-glycolic acid) (PLGA) microparticles; genetic programming; feature selection; artificial neural networks; molecular descriptors
16.  Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps 
BMC Microbiology  2008;8:101.
Little is known about bacterial transcriptional regulatory networks (TRNs). In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only ~50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms.
We designed feed-forward (FF) and bi-fan (BF) motif predictors for E. coli using multi-layer perceptron artificial neural networks (ANNs). The motif predictors were trained using a large dataset of gene expression data; the collection of motifs was extracted from the E. coli TRN. Each network motif was mapped to a vector of correlations which were computed using the gene expression profile of the elements in the motif. Thus, by combining network structural information with transcriptome data, FF and BF predictors were able to classify with a high precision of 83% and 96%, respectively, and with a high recall of 86% and 97%, respectively. These results were found when motifs were represented using different types of correlations together, i.e., Pearson, Spearman, Kendall, and partial correlation. We then applied the best predictors to hypothesize new regulations for 16 operons involved with multidrug resistance (MDR) efflux pumps, which are considered as a major bacterial mechanism to fight antimicrobial agents. As a result, the motif predictors assigned new transcription factors for these MDR proteins, turning them into high-quality candidates to be experimentally tested.
The motif predictors presented herein can be used to identify novel regulatory interactions by using microarray data. The presentation of an example motif to predictors will make them categorize whether or not the example motif is a BF, or whether or not it is an FF. This approach is useful to find new "pieces" of the TRN, when inspecting the regulation of a small set of operons. Furthermore, it shows that correlations of expression data can be used to discriminate between elements that are arranged in structural motifs and those in random sets of transcripts.
PMCID: PMC2453137  PMID: 18565227
17.  Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk 
PLoS ONE  2011;6(2):e17060.
Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.
PMCID: PMC3046173  PMID: 21386966
18.  Pharmacogenomic association study on the role of drug metabolizing, drug transporters and drug target gene polymorphisms in drug-resistant epilepsy in a north Indian population 
Indian Journal of Human Genetics  2011;17(Suppl 1):S32-S40.
In epilepsy, in spite of the best possible medications and treatment protocols, approximately one-third of the patients do not respond adequately to anti-epileptic drugs. Such interindividual variations in drug response are believed to result from genetic variations in candidate genes belonging to multiple pathways.
In the present pharmacogenetic analysis, a total of 402 epilepsy patients were enrolled. Of them, 128 were diagnosed as multiple drug-resistant epilepsy and 274 patients were diagnosed as having drug-responsive epilepsy. We selected a total of 10 candidate gene polymorphisms belonging to three major classes, namely drug transporters, drug metabolizers and drug targets. These genetic polymorphism included CYP2C9 c.430C>T (*2 variant), CYP2C9 c.1075 A>C (*3 variant), ABCB1 c.3435C>T, ABCB1c.1236C>T, ABCB1c.2677G>T/A, SCN1A c.3184 A> G, SCN2A c.56G>A (p.R19K), GABRA1c.IVS11 + 15 A>G and GABRG2 c.588C>T. Genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods, and each genotype was confirmed via direct DNA sequencing. The relationship between various genetic polymorphisms and responsiveness was examined using binary logistic regression by SPSS statistical analysis software.
CYP2C9 c.1075 A>C polymorphism showed a marginal significant difference between drug resistance and drug-responsive patients for the AC genotype (Odds ratio [OR] = 0.57, 95% confidence interval [CI] = 0.32–1.00; P = 0.05). In drug transporter, ABCB1c.2677G>T/A polymorphism, allele A was associated with drug-resistant phenotype in epilepsy patients (P = 0.03, OR = 0.31, 95% CI = 0.10-0.93). Similarly, the variant allele frequency of SCN2A c.56 G>A single nucleotide polymorphism was significantly higher in drug-resistant patients (P = 0.03; OR = 1.62, 95% CI = 1.03, 2.56). We also observed a significant difference at the genotype as well as allele frequencies of GABRA1c.IVS11 + 15 A > G polymorphism in drug-resistant patients for homozygous GG genotype (P = 0.03, OR = 1.84, 95% CI = 1.05–3.23) and G allele (P = 0.02, OR = 1.43, 95% CI = 1.05–1.95).
Our results showed that pharmacogenetic variants have important roles in epilepsy at different levels. It may be noted that multi-factorial diseases like epilepsy are also regulated by various other factors that may also be considered in the future.
PMCID: PMC3125053  PMID: 21747585
Drug resistance; epilepsy; pharmacogenomics
19.  Association between 5-HT2A, TPH1 and GNB3 genotypes and response to typical neuroleptics: a serotonergic approach 
BMC Psychiatry  2007;7:22.
Schizophrenia is a common psychiatric disease affecting about 1% of population. One major problem in the treatment is finding the right the drug for the right patients. However, pharmacogenetic results in psychiatry can seldom be replicated.
We selected three candidate genes associated with serotonergic neurotransmission for the study: serotonin 2A (5-HT2A) receptor gene, tryptophan hydroxylase 1 (TPH1) gene, and G-protein beta-3 subunit (GNB3) gene. We recruited 94 schizophrenia patients representing extremes in treatment response to typical neuroleptics: 43 were good responders and 51 were poor responders. The control group consisted of 392 healthy blood donors.
We do, in part, replicate the association between 5-HT2A T102C polymorphism and response to typical neuroleptics. In female patients, C/C genotype was significantly more common in non-responders than in responders [OR = 6.04 (95% Cl 1.67–21.93), p = 0.005] or in the control population [OR = 4.16 (95% CI 1.46–11.84), p = 0.005]. TPH1 A779C C/A genotype was inversely associated with good treatment response when compared with non-responders [OR = 0.59 (95% Cl 0.36–0.98), p = 0.030] or with the controls [OR = 0.44 (95% CI 0.23–0.86, p = 0.016], and GNB3 C825T C/T genotype showed a trend-like positive association among the male patients with a good response compared with non-responders [OR = 3.48 (95% Cl 0.92–13.25), p = 0.061], and a clearer association when compared with the controls [OR = 4.95 (95% CI 1.56–15.70), p = 0.004].
More findings on the consequences of functional polymorphisms for the role of serotonin in the development of brain and serotonergic neurotransmission are needed before more detailed hypotheses regarding susceptibility and outcome in schizophrenia can be formulated. The present results may highlight some of the biological mechanisms in different courses of schizophrenia between men and women.
PMCID: PMC1888684  PMID: 17521439
20.  Evaluation of the Relationship between 5-HTT and MAO Gene Polymorphisms, Mood and Level of Anxiety among Postmenopausal Women 
Objective: The aim of this study was to analyze how mood and anxiety level are related to the functional genetic polymorphism in the promoter region of SLC6A4 (5-HTTLPR) and the 30-bp VNTR polymorphism in the MAO A promoter region. Methods: The study involved 272 postmenopausal women from Poland. The authors employed the State-Trait Anxiety Inventory for measuring levels of anxiety, the Mood Adjective Check List for measuring mood, and genetic tests. Results: Analysis did not show any statistically significant differences in the mean levels of anxiety, and mood disorders in women in relation to genotypes of the 5-HTTLPR (SLC6A4) polymorphism and the 30-bp VNTR polymorphism in the MAO A promoter region. However, these problems were more severe among women with s/s genotype. In the case of MAO A gene polymorphism, the level of anxiety was higher in women with a 4/4 genotype. Conclusions: The study did not prove the possibility of the identification of homogeneous groups of women with an elevated risk of developing anxiety and mood disorders during the post-menopausal period. Nevertheless, it showed that respondents with s/s genotype of the 44-bp polymorphism in the 5-HTT (SLC6A4) promoter region had the highest average anxiety levels both as a state and as a trait. Furthermore, the analysis of the 30-bp VNTR polymorphism in the MAO A promoter region demonstrated slight differences in anxiety levels between the women, indicating that those with a 4/4 genotype had higher severity of anxiety symptoms.
PMCID: PMC4306861  PMID: 25547397
mood disorders; postmenopause; 5HTTLPR; MAO A; anxiety
21.  A preliminary study on automated freshwater algae recognition and classification system 
BMC Bioinformatics  2012;13(Suppl 17):S25.
Freshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap.
The development of the automated freshwater algae detection system involved image preprocessing, segmentation, feature extraction and classification by using Artificial neural networks (ANN). Image preprocessing was used to improve contrast and remove noise. Image segmentation using canny edge detection algorithm was then carried out on binary image to detect the algae and its boundaries. Feature extraction process was applied to extract specific feature parameters from algae image to obtain some shape and texture features of selected algae such as shape, area, perimeter, minor and major axes, and finally Fourier spectrum with principal component analysis (PCA) was applied to extract some of algae feature texture. Artificial neural network (ANN) is used to classify algae images based on the extracted features. Feed-forward multilayer perceptron network was initialized with back propagation error algorithm, and trained with extracted database features of algae image samples. System's accuracy rate was obtained by comparing the results between the manual and automated classifying methods. The developed system was able to identify 93 images of selected freshwater algae genera from a total of 100 tested images which yielded accuracy rate of 93%.
This study demonstrated application of automated algae recognition of five genera of freshwater algae. The result indicated that MLP is sufficient, and can be used for classification of freshwater algae. However for future studies, application of support vector machine (SVM) and radial basis function (RBF) should be considered for better classifying as the number of algae species studied increases.
PMCID: PMC3521397  PMID: 23282059
22.  Endophenotypes and serotonergic polymorphisms associated with treatment response in obsessive-compulsive disorder 
Clinics  2012;67(4):335-340.
Approximately 40-60% of obsessive-compulsive disorder patients are nonresponsive to serotonin reuptake inhibitors. Genetic markers associated with treatment response remain largely unknown. We aimed (1) to investigate a possible association of serotonergic polymorphisms in obsessive-compulsive disorder patients and therapeutic response to selective serotonin reuptake inhibitors and (2) to examine the relationship between these polymorphisms and endocrine response to intravenous citalopram challenge in responders and non-responders to serotonin reuptake inhibitors and in healthy volunteers.
Patients with obsessive-compulsive disorder were classified as either responders or non-responders after long-term treatment with serotonin reuptake inhibitors, and both groups were compared with a control group of healthy volunteers. The investigated genetic markers were the G861C polymorphism of the serotonin receptor 1Dβ gene and the T102C and C516T polymorphisms of the serotonin receptor subtype 2A gene.
The T allele of the serotonin receptor subtype 2A T102C polymorphism was more frequent among obsessive-compulsive disorder patients (responders and non-responders) than in the controls (p<0.01). The CC genotype of the serotonin receptor subtype 2A C516T polymorphism was more frequent among the non-responders than in the responders (p<0.01). The CC genotype of the serotonin receptor subtype 1Dβ G681C polymorphism was associated with higher cortisol and prolactin responses to citalopram (p<0.01 and p<0.001, respectively) and with a higher platelet-rich plasma serotonin concentration among the controls (p<0.05). However, this pattern was not observed in the non-responders with the same CC genotype after chronic treatment with serotonin reuptake inhibitors. This CC homozygosity was not observed in the responders.
PMCID: PMC3317252  PMID: 22522758
Pharmacogenetics; Serotonin Receptor Subtype 1Dβ; Serotonin Receptor Subtype 2A; Citalopram; Challenge Test
23.  The Role of Abcb5 Alleles in Susceptibility to Haloperidol-Induced Toxicity in Mice and Humans 
PLoS Medicine  2015;12(2):e1001782.
We know very little about the genetic factors affecting susceptibility to drug-induced central nervous system (CNS) toxicities, and this has limited our ability to optimally utilize existing drugs or to develop new drugs for CNS disorders. For example, haloperidol is a potent dopamine antagonist that is used to treat psychotic disorders, but 50% of treated patients develop characteristic extrapyramidal symptoms caused by haloperidol-induced toxicity (HIT), which limits its clinical utility. We do not have any information about the genetic factors affecting this drug-induced toxicity. HIT in humans is directly mirrored in a murine genetic model, where inbred mouse strains are differentially susceptible to HIT. Therefore, we genetically analyzed this murine model and performed a translational human genetic association study.
Methods and Findings
A whole genome SNP database and computational genetic mapping were used to analyze the murine genetic model of HIT. Guided by the mouse genetic analysis, we demonstrate that genetic variation within an ABC-drug efflux transporter (Abcb5) affected susceptibility to HIT. In situ hybridization results reveal that Abcb5 is expressed in brain capillaries, and by cerebellar Purkinje cells. We also analyzed chromosome substitution strains, imaged haloperidol abundance in brain tissue sections and directly measured haloperidol (and its metabolite) levels in brain, and characterized Abcb5 knockout mice. Our results demonstrate that Abcb5 is part of the blood-brain barrier; it affects susceptibility to HIT by altering the brain concentration of haloperidol. Moreover, a genetic association study in a haloperidol-treated human cohort indicates that human ABCB5 alleles had a time-dependent effect on susceptibility to individual and combined measures of HIT. Abcb5 alleles are pharmacogenetic factors that affect susceptibility to HIT, but it is likely that additional pharmacogenetic susceptibility factors will be discovered.
ABCB5 alleles alter susceptibility to HIT in mouse and humans. This discovery leads to a new model that (at least in part) explains inter-individual differences in susceptibility to a drug-induced CNS toxicity.
Gary Peltz and colleagues examine the role of ABCB5 alleles in haloperidol-induced toxicity in a murine genetic model and humans treated with haloperidol.
Editors' Summary
The brain is the control center of the human body. This complex organ controls thoughts, memory, speech, and movement, it is the seat of intelligence, and it regulates the function of many organs. The brain comprises many different parts, all of which work together but all of which have their own special functions. For example, the forebrain is involved in intellectual activities such as thinking whereas the hindbrain controls the body’s vital functions and movements. Messages are passed between the various regions of the brain and to other parts of the body by specialized cells called neurons, which release and receive signal molecules known as neurotransmitters. Like all the organs in the body, blood vessels supply the brain with the oxygen, water, and nutrients it needs to function. Importantly, however, the brain is protected from infectious agents and other potentially dangerous substances circulating in the blood by the “blood-brain barrier,” a highly selective permeability barrier that is formed by the cells lining the fine blood vessels (capillaries) within the brain.
Why Was This Study Done?
Although drugs have been developed to treat various brain disorders, more active and less toxic drugs are needed to improve the treatment of many if not most of these conditions. Unfortunately, relatively little is known about how the blood-brain barrier regulates the entry of drugs into the brain or about the genetic factors that affect the brain’s susceptibility to drug-induced toxicities. It is not known, for example, why about half of patients given haloperidol—a drug used to treat psychotic disorders (conditions that affect how people think, feel, or behave)—develop tremors and other symptoms caused by alterations in the brain region that controls voluntary movements. Here, to improve our understanding of how drugs enter the brain and impact its function, the researchers investigate the genetic factors that affect haloperidol-induced toxicity by genetically analyzing several inbred mouse strains (every individual in an inbred mouse strain is genetically identical) with different susceptibilities to haloperidol-induced toxicity and by undertaking a human genetic association study (a study that looks for non-chance associations between specific traits and genetic variants).
What Did the Researchers Do and Find?
The researchers used a database of genetic variants called single nucleotide polymorphisms (SNPs) and a computational genetic mapping approach to show first that variations within the gene encoding Abcb5 affected susceptibility to haloperidol-induced toxicity (indicated by changes in the length of time taken by mice to move their paws when placed on an inclined wire-mesh screen) among inbred mouse strains. Abcb5 is an ATP-binding cassette transporter, a type of protein that moves molecules across cell membranes. The researchers next showed that Abcb5 is expressed in brain capillaries, which is the location of the blood-brain barrier. Abcb5 was also expressed in cerebellar Purkinje cells, which help to control motor (intentional) movements. They also measured the measured the effect of haloperidol and the haloperidol concentration in brain tissue sections in mice that were genetically engineered to make no Abcb5 (Abcb5 knockout mice). Finally, the researchers investigated whether specific alleles (alternative versions) of ABCB5 are associated with haloperidol-induced toxicity in people. Among a group of 85 patients treated with haloperidol for a psychotic illness, one specific ABCB5 allele was associated with haloperidol-induced toxicity during the first few days of treatment.
What Do These Findings Mean?
These findings indicate that Abcb5 is a component of the blood-brain barrier in mice and suggest that genetic variants in the gene encoding this protein underlie, at least in part, the differences in susceptibility to haloperidol-induced toxicity seen among inbred mice strains. Moreover, the human genetic association study indicates that a specific ABCB5 allele also affects the susceptibility of people to haloperidol-induced toxicity. The researchers note that other ABCB5 alleles or other genetic factors that affect haloperidol-induced toxicity in people might emerge if larger groups of patients were studied. However, based on their findings, the researchers propose a new model for the genetic mechanisms that underlie inter-individual and cell type-specific differences in susceptibility to haloperidol-induced brain toxicity. If confirmed in future studies, this model might facilitate the development of more effective and less toxic drugs to treat a range of brain disorders.
Additional Information
Please access these websites via the online version of this summary at
The US National Institute of Neurological Disorders and Stroke provides information about a wide range of brain diseases (in English and Spanish); its fact sheet “Brain Basics: Know Your Brain” is a simple introduction to the human brain; its “Blueprint Neurotherapeutics Network” was established to develop new drugs for disorders affecting the brain and other parts of the nervous system
MedlinePlus provides links to additional resources about brain diseases and their treatment (in English and Spanish)
Wikipedia provides information about haloperidol, about ATP-binding cassette transporters and about genetic association (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
PMCID: PMC4315575  PMID: 25647612
24.  Prediction of periventricular leukomalacia. Part II 
The objective of Part II is to analyze the dataset of extracted hemodynamic features (Case 3 of Part I) through computational intelligence (CI) techniques for identification of potential prognostic factors for periventricular leukomalacia (PVL) occurrence in neonates with congenital heart disease.
The extracted features (Case 3 dataset of Part I) were used as inputs to CI based classifiers, namely, multi-layer perceptron (MLP) and probabilistic neural network (PNN) in combination with genetic algorithms (GA) for selection of the most suitable features predicting the occurrence of PVL. The selected features were next used as inputs to a decision tree (DT) algorithm for generating easily interpretable rules of PVL prediction.
Prediction performance for two CI based classifiers, MLP and PNN coupled with GA are presented for different number of selected features. The best prediction performances were achieved with 6 and 7 selected features. The prediction success was 100% in training and the best ranges of sensitivity (SN), specificity (SP) and accuracy (AC) in test were 60-73%, 74-84% and 71-74%, respectively. The identified features when used with the DTalgorithm gave best SN, SP and AC in the ranges of 87-90% in training and 80-87%, 74-79% and 79-82% in test. Among the variables selected in CI, systolic and diastolic blood pressures, and pCO2 figured prominently similar to Part I. Decision tree based rules for prediction of PVL occurrence were obtained using the CI selected features.
The proposed approach combines the generalization capability of CI based feature selection approach and generation of easily interpretable classification rules of the decision tree. The combination of CI techniques with DT gave substantially better test prediction performance than using CI and DT separately.
PMCID: PMC2714881  PMID: 19162456
Congenital heart disease; Computational intelligence; Data mining; Decision tree; Genetic algorithms; Neural networks; Periventricular leukomalacia
25.  Artificial Neural Networks: A Novel Approach to Analysing the Nutritional Ecology of a Blowfly Species, Chrysomya megacephala  
Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies.
PMCID: PMC3014792  PMID: 20569135
insect bionomics; larval density; life-history; mass rearing

Results 1-25 (933549)