EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features

Uddin, Md. Raihan; Sharma, Alok; Farid, Dewan Md.; Rahman, Md. Mahmudur; Dehzangi, Abdollah; Shatabda, Swakkhar

doi:10.1016/j.jtbi.2018.02.002

Cited by 32 publications

(11 citation statements)

References 104 publications

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“…Support Vector Machine (SVM) was first proposed by Vapnik et al in 1995 [45], and is a ML method based on Vapnik-Chervonenkis (VC) dimension theory and the principle of structural risk minimization. It was first applied to classical classification problems and showing promise in solving nonlinear and high dimensional problems, then the method was applied to common regression problems [46, 47]. An SVM applied to nonlinear regression is called Support Vector Regression (SVR).…”

Section: Methodsmentioning

confidence: 99%

Regional level influenza study based on Twitter and machine learning method

Xue

Bai

et al. 2019

PLoS ONE

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The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control’s (CDC’s) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu.

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Section: Methodsmentioning

confidence: 99%

Regional level influenza study based on Twitter and machine learning method

Xue

Bai

et al. 2019

PLoS ONE

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“…However, all these methods use information from the primary sequence or the frequencies of amino acid residues or nucleotides in each side of the window. Secondary Structural information was earlier used with primary sequence information in problems of predicting protein binding sites, succinylation, phosphorylation, drug‐target interaction, DNA‐binding protein identification, and protein sub‐cellular localization . To the best of our knowledge, secondary structural information and physico‐chemical properties are not explored to predict lysine glycation of peptides.…”

Section: Methodsmentioning

confidence: 99%

iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features

et al. 2018

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Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly-SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly-SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/.

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“…These features are considered important source to provide information about the local interaction of amino acids along the protein sequence. Also, they have been used in different studies to tackle different problems in protein science and attained promising results 53–56 . The subsequent sections below discuss these structural properties.…”

Section: Methodsmentioning

confidence: 99%

PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids

Chandra

Sharma

Dehzangi

et al. 2018

Sci Rep

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The biological process known as post-translational modification (PTM) contributes to diversifying the proteome hence affecting many aspects of normal cell biology and pathogenesis. There have been many recently reported PTMs, but lysine phosphoglycerylation has emerged as the most recent subject of interest. Despite a large number of proteins being sequenced, the experimental method for detection of phosphoglycerylated residues remains an expensive, time-consuming and inefficient endeavor in the post-genomic era. Instead, the computational methods are being proposed for accurately predicting phosphoglycerylated lysines. Though a number of predictors are available, performance in detecting phosphoglycerylated lysine residues is still limited. In this paper, we propose a new predictor called PhoglyStruct that utilizes structural information of amino acids alongside a multilayer perceptron classifier for predicting phosphoglycerylated and non-phosphoglycerylated lysine residues. For the experiment, we located phosphoglycerylated and non-phosphoglycerylated lysines in our employed benchmark. We then derived and integrated properties such as accessible surface area, backbone torsion angles, and local structure conformations. PhoglyStruct showed significant improvement in the ability to detect phosphoglycerylated residues from non-phosphoglycerylated ones when compared to previous predictors. The sensitivity, specificity, accuracy, Mathews correlation coefficient and AUC were 0.8542, 0.7597, 0.7834, 0.5468 and 0.8077, respectively. The data and Matlab/Octave software packages are available at https://github.com/abelavit/PhoglyStruct.

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EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features

Cited by 32 publications

References 104 publications

Regional level influenza study based on Twitter and machine learning method

Regional level influenza study based on Twitter and machine learning method

iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features

PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids

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