EURASIP journal on bioinformatics & systems biology最新文献

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On biometric systems: electrocardiogram Gaussianity and data synthesis. 生物识别系统：心电图高斯性和数据合成。

EURASIP journal on bioinformatics & systems biology Pub Date : 2017-02-21 eCollection Date: 2017-12-01 DOI: 10.1186/s13637-017-0056-2

Wael Louis, Shahad Abdulnour, Sahar Javaher Haghighi, Dimitrios Hatzinakos

{"title":"On biometric systems: electrocardiogram Gaussianity and data synthesis.","authors":"Wael Louis, Shahad Abdulnour, Sahar Javaher Haghighi, Dimitrios Hatzinakos","doi":"10.1186/s13637-017-0056-2","DOIUrl":"https://doi.org/10.1186/s13637-017-0056-2","url":null,"abstract":"Electrocardiogram is a slow signal to acquire, and it is prone to noise. It can be inconvenient to collect large number of ECG heartbeats in order to train a reliable biometric system; hence, this issue might result in a small sample size phenomenon which occurs when the number of samples is much smaller than the number of observations to model. In this paper, we study ECG heartbeat Gaussianity and we generate synthesized data to increase the number of observations. Data synthesis, in this paper, is based on our hypothesis, which we support, that ECG heartbeats exhibit a multivariate normal distribution; therefore, one can generate ECG heartbeats from such distribution. This distribution is deviated from Gaussianity due to internal and external factors that change ECG morphology such as noise, diet, physical and psychological changes, and other factors, but we attempt to capture the underlying Gaussianity of the heartbeats. When this method was implemented for a biometric system and was examined on the University of Toronto database of 1012 subjects, an equal error rate (EER) of 6.71% was achieved in comparison to 9.35% to the same system but without data synthesis. Dimensionality reduction is widely examined in the problem of small sample size; however, our results suggest that using the proposed data synthesis outperformed several dimensionality reduction techniques by at least 3.21% in EER. With small sample size, classifier instability becomes a bigger issue and we used a parallel classifier scheme to reduce it. Each classifier in the parallel classifier is trained with the same genuine dataset but different imposter datasets. The parallel classifier has reduced predictors' true acceptance rate instability from 6.52% standard deviation to 1.94% standard deviation.","PeriodicalId":101453,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":"2017 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319943/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148914851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computing interaction probabilities in signaling networks. 信令网络中交互概率的计算。

EURASIP journal on bioinformatics & systems biology Pub Date : 2015-11-11 eCollection Date: 2015-12-01 DOI: 10.1186/s13637-015-0031-8

Haitham Gabr, Juan Carlos Rivera-Mulia, David M Gilbert, Tamer Kahveci

{"title":"Computing interaction probabilities in signaling networks.","authors":"Haitham Gabr, Juan Carlos Rivera-Mulia, David M Gilbert, Tamer Kahveci","doi":"10.1186/s13637-015-0031-8","DOIUrl":"https://doi.org/10.1186/s13637-015-0031-8","url":null,"abstract":"Biological networks inherently have uncertain topologies. This arises from many factors. For instance, interactions between molecules may or may not take place under varying conditions. Genetic or epigenetic mutations may also alter biological processes like transcription or translation. This uncertainty is often modeled by associating each interaction with a probability value. Studying biological networks under this probabilistic model has already been shown to yield accurate and insightful analysis of interaction data. However, the problem of assigning accurate probability values to interactions remains unresolved. In this paper, we present a novel method for computing interaction probabilities in signaling networks based on transcription levels of genes. The transcription levels define the signal reachability probability between membrane receptors and transcription factors. Our method computes the interaction probabilities that minimize the gap between the observed and the computed signal reachability probabilities. We evaluate our method on four signaling networks from the Kyoto Encyclopedia of Genes and Genomes (KEGG). For each network, we compute its edge probabilities using the gene expression profiles for seven major leukemia subtypes. We use these values to analyze how the stress induced by different leukemia subtypes affects signaling interactions.","PeriodicalId":101453,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":"2015 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2015-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13637-015-0031-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89721562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

40-Hz ASSR fusion classification system for observing sleep patterns. 用于观察睡眠模式的40hz ASSR融合分类系统。

EURASIP journal on bioinformatics & systems biology Pub Date : 2015-02-05 eCollection Date: 2015-12-01 DOI: 10.1186/s13637-014-0021-2

Gulzar A Khuwaja, Sahar Javaher Haghighi, Dimitrios Hatzinakos

{"title":"40-Hz ASSR fusion classification system for observing sleep patterns.","authors":"Gulzar A Khuwaja, Sahar Javaher Haghighi, Dimitrios Hatzinakos","doi":"10.1186/s13637-014-0021-2","DOIUrl":"https://doi.org/10.1186/s13637-014-0021-2","url":null,"abstract":"This paper presents a fusion-based neural network (NN) classification algorithm for 40-Hz auditory steady state response (ASSR) ensemble averaged signals which were recorded from eight human subjects for observing sleep patterns (wakefulness W0 and deep sleep N3 or slow wave sleep SWS). In SWS, sensitivity to pain is the lowest relative to other sleep stages and arousal needs stronger stimuli. 40-Hz ASSR signals were extracted by averaging over 900 sweeps on a 30-s window. Signals generated during N3 deep sleep state show similarities to those produced when general anesthesia is given to patients during clinical surgery. Our experimental results show that the automatic classification system used identifies sleep states with an accuracy rate of 100% when the training and test signals come from the same subjects while its accuracy is reduced to 97.6%, on average, when signals are used from different training and test subjects. Our results may lead to future classification of consciousness and wakefulness of patients with 40-Hz ASSR for observing the depth and effects of general anesthesia (DGA).","PeriodicalId":101453,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":"2015 ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2015-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13637-014-0021-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89721561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5