MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins

Ge, Fang; Zhu, Yiheng; Data, Jian Xu; Arif, Muhammad; Song, Jiangning; Yu, Dong‐Jun

doi:10.1016/j.csbj.2021.11.024

Cited by 19 publications

(16 citation statements)

References 82 publications

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“…This method helps reduce the complexity of the PSSM from L × 20 to L × 3. There is also a weighting method for the PSSM using Gaussian distribution implemented by Ge et al 12 …”

Section: Methodsmentioning

confidence: 99%

“…In bioinformatics, a PSSM is a common method to encode protein sequences. − Although this encode provides a lot of useful information for research, when using a PSSM to encode data, the common problem is that the difference in the amino acid number of the protein sequences leads to the difference in the size of the PSSM data representing the sequences. Therefore, there have been many studies performing transformations to standardize the size of the PSSM.…”

Section: Methodsmentioning

confidence: 99%

“…This method helps reduce the complexity of the PSSM from L × 20 to L × 3. There is also a weighting method for the PSSM using Gaussian distribution implemented by Ge et al 12 For TF classification problem, we use the PSSM scheme to encode protein sequences, and then, PSSMs are used to train the CNN model. PSSMs have the form P = P ij : i = 1, ..., L and j = 1,..., 20 where L is the length of the protein sequence and each P ij represents the score of the jth amino acid in the 20 amino acids and the acid the ith amino acid in the query sequence.…”

Section: Position-specific Scoring Matrixmentioning

confidence: 99%

See 2 more Smart Citations

Identifying Transcription Factors That Prefer Binding to Methylated DNA Using Reduced G-Gap Dipeptide Composition

et al. 2022

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Transcription factors (TFs) play an important role in gene expression and regulation of 3D genome conformation. TFs have ability to bind to specific DNA fragments called enhancers and promoters. Some TFs bind to promoter DNA fragments which are near the transcription initiation site and form complexes that allow polymerase enzymes to bind to initiate transcription. Previous studies showed that methylated DNAs had ability to inhibit and prevent TFs from binding to DNA fragments. However, recent studies have found that there were TFs that could bind to methylated DNA fragments. The identification of these TFs is an important steppingstone to a better understanding of cellular gene expression mechanisms. However, as experimental methods are often time-consuming and labor-intensive, developing computational methods is essential. In this study, we propose two machine learning methods for two problems: (1) identifying TFs and (2) identifying TFs that prefer binding to methylated DNA targets (TFPMs). For the TF identification problem, the proposed method uses the position-specific scoring matrix for data representation and a deep convolutional neural network for modeling. This method achieved 90.56% sensitivity, 83.96% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.9596 on an independent test set. For the TFPM identification problem, we propose to use the reduced g -gap dipeptide composition for data representation and the support vector machine algorithm for modeling. This method achieved 82.61% sensitivity, 64.86% specificity, and an AUC of 0.8486 on another independent test set. These results are higher than those of other studies on the same problems.

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“…This method helps reduce the complexity of the PSSM from L × 20 to L × 3. There is also a weighting method for the PSSM using Gaussian distribution implemented by Ge et al 12 …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Position-specific Scoring Matrixmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Transcription Factors That Prefer Binding to Methylated DNA Using Reduced G-Gap Dipeptide Composition

et al. 2022

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“…XGBoost is a relative novel supervised algorithm based on gradient boosting decision tree (GBDT), which adds regularization term to avoid over‐fitting and performs second‐order Taylor expansion on the loss function to improve modeling accuracy. Due to its excellent performance, XGBoost has been applied to many engineering practices such as tuberculosis detection based on chest X‐ray (Rahman et al, 2021), probabilistic solar irradiance forecasting (Li et al, 2022) and prediction of mutations in transmembrane proteins (Ge et al, 2021). However, there were few reports on its application to the regression problem of spectral data.…”

Section: Methodsmentioning

confidence: 99%

Nondestructive detection of S‐ovalbumin content in eggs using portable NIR spectrometer and MPA‐CARS

Yao

Sun

Cheng

et al. 2022

J Food Process Engineering

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The feasibility of applying a low‐cost and portable NIR spectrometer to detect the S‐ovalbumin content of eggs was investigated in this study, and a model population analysis based competitive adaptive reweighted sampling (MPA‐CARS) algorithm was proposed to reduce the dimensionality of spectral data. The NIR spectra of egg samples with different storage periods were collected in the wavelength range of 900–1700 nm. Standard normal variate (SNV) and Savitzky–Golay1st derivative were used to preprocess the raw spectral data. Then, CARS and the proposed MPA‐CARS were applied to select effective wavelengths from the whole spectral range. Statistical results showed that MPA‐CARS had better feature extraction performance than CARS and the number of selected feature wavelengths was less. Support vector machine (SVM), back propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were used to establish calibration models for predicting S‐ovalbumin content, in which the simplified XGBoost model based on MPA‐CARS feature wavelengths yielded the best performance, with R2P of 0.906 and RMSEP of 7.799%. Therefore, portable NIR has the potential to be a useful tool for S‐ovalbumin content detection. This could help food processing industry to arrange miniaturized NIR sensors to detect egg quality at different points in the egg supply chain. Practical applications S‐ovalbumin content is a freshness index of eggs and can affect the quality of processed foods. This study demonstrated the feasibility of using portable NIR spectrometer to detect the S‐ovalbumin content of eggs, which can help food processing agencies to implement low‐cost NIR sensors to detect egg quality at different stages of the supply chain.

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“…The application of NGS technologies such as whole genome/exome sequencing (WGS/WES) has been ( Ng et al, 2009 ; Ng et al, 2010 ), and continues to be ( Macken et al, 2021 ; Marom et al, 2021 ; Usmani et al, 2021 ) instrumental in the identification of novel mutations including those leading to splicing defects. Further, this mutational information can be efficiently analyzed using advanced algorithms to predict non-tolerated/pathogenic changes ( Ng and Henikoff, 2003 ; Schwarz et al, 2010 ; Adzhubei et al, 2013 ; Ioannidis et al, 2016 ; Rentzsch et al, 2019 ; Ge et al, 2021 ) and by molecular dynamics to infer putative structural alterations ( Kellogg et al, 2011 ; Adolf-Bryfogle and Dunbrack, 2013 ).…”

Section: Candidate Components For An Updated Multidisciplinary Toolbo...mentioning

confidence: 99%

The Future of Genetic Disease Studies: Assembling an Updated Multidisciplinary Toolbox

Ramadesikan

Lee

Aguilar

2022

Front. Cell Dev. Biol.

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MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins

Abstract: Graphical abstract

Cited by 19 publications

References 82 publications

Identifying Transcription Factors That Prefer Binding to Methylated DNA Using Reduced G-Gap Dipeptide Composition

Identifying Transcription Factors That Prefer Binding to Methylated DNA Using Reduced G-Gap Dipeptide Composition

Nondestructive detection of S‐ovalbumin content in eggs using portable NIR spectrometer and MPA‐CARS

The Future of Genetic Disease Studies: Assembling an Updated Multidisciplinary Toolbox

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