Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

Bulashevska, Alla; Eils, Roland

doi:10.1186/1471-2105-7-298

Cited by 71 publications

(14 citation statements)

References 32 publications

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“…N and n represent the number of all the protein sequences, the number of each type of protein sequences, respectively. We list the results in Tables III and IV about the data set 98 proteins and 317 data set proteins in jackknife Table III shows the result based the proposed method is higher than the covariant method, 21 the BC method, 24 the Hens_ BC 24 method, the ID_SVM method, 25 and Liao's method. 17 Table IV shows our result is higher than the ID method, 22 ID_SVM method 25 and Liao's method.…”

Section: Resultsmentioning

confidence: 93%

New Method Expression of Protein Sequence and Its Application for Protein Sub-Cellular Localization Prediction

He¹,

Zheng²,

Liao³

et al. 2014

jnl of comp & theo nano

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Graphical representation of protein sequence is to provide intuitive overall graphs as well as useful insights for performing large-scale analysis. Some methods have been proposed to analyze the similarities/dissimilarities among different sequences. Here, we consider a novel 3D graphical representation of protein sequences. It is based on a substitution matrix and the frequency of the 20 common amino acids. According to this representation, we give a 2D distance formula to calculate the distance of different protein sequences for similarity analysis. Finally, the proposed method is used to predict protein cellular localization.

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Section: Resultsmentioning

confidence: 93%

New Method Expression of Protein Sequence and Its Application for Protein Sub-Cellular Localization Prediction

He¹,

Zheng²,

Liao³

et al. 2014

jnl of comp & theo nano

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“…c Comes from Zhang et al (2006). d Comes from Bulashevska and Eils (2006). e Comes from Zhou et al (2008).…”

Section: Resultsmentioning

confidence: 98%

“…In the past 5 years, several algorithms such as covariant discriminant function (Zhou and Doctor 2003), support vector machine (SVM) (Huang and Shi 2005;Zhang et al 2006;Zhou et al 2008;Shi et al 2008), Bayesian classifier (Bulashevska and Eils 2006), increment of diversity (ID) (Chen and Li 2007a), increment of diversity combined with support vector machine (ID_SVM) (Chen and Li 2007b) and fuzzy K-nearest neighbor (FKNN) (Jiang et al 2008;Ding and Zhang 2008) have been proposed to predict subcellular localization of apoptosis protein based on various amino acid composition or pseudo amino acid composition. The pseudo amino acid composition (PseAAC) was firstly proposed by Chou to efficiently improve prediction quantity of protein subcellular localization (Chou 2001;Chou and Shen 2007a).…”

mentioning

confidence: 99%

Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition

et al. 2009

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Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou's pseudo amino acid composition (PseAAC), a total of 317 apoptosis proteins are predicted by support vector machine (SVM). The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%.

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“…Time-course gene expression data are collected at a series of time points during a biological process of interest and thus reflect the dynamic activity of genes during this process. At present, some methods [31][32][33][34][35][36] use a threshold to determine whether genes are significantly expressed. In this study, the 3-sigma principle was used to compute the active threshold as proposed in ref.…”

Section: Definition Of Active Proteins and Dynamic Interactionsmentioning

confidence: 99%

A Novel Core‐Attachment–Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Xiao

Luo

et al. 2019

Proteomics

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Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein–protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein‐complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core‐attachment–based method named CO‐DPC to detect dynamic protein complexes is presented. First, CO‐DPC selects active proteins according to gene expression profiles and the 3‐sigma principle, and constructs dynamic PPI networks based on the co‐expression principle and PPI networks. Second, CO‐DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO‐DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.

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Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

Cited by 71 publications

References 32 publications

New Method Expression of Protein Sequence and Its Application for Protein Sub-Cellular Localization Prediction

New Method Expression of Protein Sequence and Its Application for Protein Sub-Cellular Localization Prediction

Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition

A Novel Core‐Attachment–Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

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