The dynamic mechanism of a novel stochastic neural firing pattern observed in a real biological system

Shang, Huijie; Jiang, Zhongting; Xu, Rongbin; Wang, Dong; Wu, Peng; Chen, Yuehui

doi:10.1016/j.cogsys.2018.04.009

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…Many computational methods have been proposed to classify cancer subtypes [17–21], however most of them generate the model by only employing gene expression data. Gene expression data is used as input to DFNForest classifier [46].…”

Section: Discussionmentioning

confidence: 99%

“…These sequencing data provide an unprecedented opportunity to study cancer subtype at the molecular level by using multi-omics data [15, 16]. Many computational methods have been proposed to classify cancer subtypes [17–21], however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data provides better cancer subtype classification in recent years [22–24].…”

Section: Introductionmentioning

confidence: 99%

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A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data

Chen

et al. 2019

BMC Bioinformatics

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BackgroundCancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification.ResultsA new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods.ConclusionThe new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data

Chen

et al. 2019

BMC Bioinformatics

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“…The synergistic effect of noise and system is beneficial to the output of system signals, which is different from people's intuitive understanding of noise [17,18]. Many scholars have studied the effects of white noise, additive noise, phase noise, conductance-based noise and non-Gaussian noise on the random discharge of neurons, suggesting that noise has an important influence on the discharge activity of neurons, so it may participate in the information processing of nervous system, which is helpful to explore the function of randomness in nerve stimulation [19][20][21][22][23][24]. Studying the rhythm of nerve discharge and the role of noise in nervous system movement is helpful to discover the general law of nerve movement and the existence and significance of noise in life activities.…”

Section: Introductionmentioning

confidence: 99%

“…V I , V K , V L represent the corresponding equilibrium potentials. The parameter settings in this paper are taken from [22].…”

Section: Introductionmentioning

confidence: 99%

Effect of potassium channel noise on nerve discharge based on the Chay model

Jiang

Wang

Shang

et al. 2020

THC

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BACKGROUND: The nervous system senses and transmits information through the firing behavior of neurons, and this process is affected by various noises. However, in the previous study of the influence of noise on nerve discharge, the channel of some noise effects is not clear, and the difference from other noises was not examined. OBJECTIVE: To construct ion channel noise which is more biologically significant, and to clarify the basic characteristics of the random firing rhythm of neurons generated by different types of noise acting on ion channels. Method: Based on the dynamics of the ion channel, we constructed ion channel noise. We simulated the nerve discharge based on the Chay model of potassium ion channel noise, and used the nonlinear time series analysis method to measure the certainty and randomness of nerve discharge. RESULTS: In the Chay model with potassium ion noise, the chaotic rhythm defined by the original model could be effectively unified with the random rhythm simulated by the previous random Chay model into a periodic bifurcation process. CONCLUSION: This method clarified the influence of ion channel noise on nerve discharge, better understood the randomness of nerve discharge and provided a more reasonable explanation for the mechanism of nerve discharge.

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“…As protein subcellular localization (PSL) is highly correlated with protein function, effective PSL prediction methods provide information for understanding protein functions that is essential for several research applications such as in molecular biology [1, 2], cell biology [3, 4], and neuroscience [5], as well as in clinical medicine [6, 7, 8]. The prediction and recognition of PSL using bioinformatics methods is one of the fundamental objectives of proteomics and cell biology [9], as PSL information is crucial for discovering protein functions toward helping to understand the complex cellular pathways involved in the process of biological regulation.…”

Section: Introductionmentioning

confidence: 99%

Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets

Jiang

Wang

et al. 2019

THC

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BACKGROUND: For a protein to execute its function, ensuring its correct subcellular localization is essential. In addition to biological experiments, bioinformatics is widely used to predict and determine the subcellular localization of proteins. However, single-feature extraction methods cannot effectively handle the huge amount of data and multisite localization of proteins. Thus, we developed a pseudo amino acid composition (PseAAC) method and an entropy density technique to extract feature fusion information from subcellular multisite proteins. OBJECTIVE: Predicting multiplex protein subcellular localization and achieve high prediction accuracy. METHOD: To improve the efficiency of predicting multiplex protein subcellular localization, we used the multi-label k-nearest neighbors algorithm and assigned different weights to various attributes. The method was evaluated using several performance metrics with a dataset consisting of protein sequences with single-site and multisite subcellular localizations. RESULTS: Evaluation experiments showed that the proposed method significantly improves the optimal overall accuracy rate of multiplex protein subcellular localization. CONCLUSION: This method can help to more comprehensively predict protein subcellular localization toward better understanding protein function, thereby bridging the gap between theory and application toward improved identification and monitoring of drug targets.

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The dynamic mechanism of a novel stochastic neural firing pattern observed in a real biological system

Cited by 16 publications

References 31 publications

A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data

A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data

Effect of potassium channel noise on nerve discharge based on the Chay model

Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets

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