Identifying DNA-binding proteins based on multi-features fusion and LASSO feature selection

Zhang, Shengli; Zhu, Fu; Yu, Qianhao; Zhu, Xiaoqin

doi:10.22541/au.159405844.42929954

Cited by 10 publications

(13 citation statements)

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“…This method not only helps ensure the characteristic vector size for protein sequences but also improves the efficiency of the subsequent machine learning model. There is also an auto-cross covariance transform and discrete wavelet transform used by Zhang et al 13 Due to the rather large PSSM data with a size of L × 20 ( L is the protein chain length), Chen et al 15 proposed an secondary structure element (SSE)-PSSM method that not only reduces the size of the feature vector but also improves performance of predicting the secondary structure of proteins with the most important modification being the SSE transformation. After searching for strings that are similar to the query string, they first converted the strings to an SSE form and then computed the position propensity matrix and finally the PSSM.…”

Section: Methodsmentioning

confidence: 99%

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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Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…(2016) explored the features of the popular regression methods, OLS regression, ridge regression and the LASSO regression. Zhang et al (2021) . (2021) used LASSO dimensionality reduction method to conduct experiments on the combination of feature sub models to obtain the best top-level feature number, thus providing support for the effective prediction of DNA binding proteins.…”

Section: Related Workmentioning

confidence: 99%

MLP-Based Regression Prediction Model For Compound Bioactivity

Qin

Shi

et al. 2022

Front. Bioeng. Biotechnol.

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The development of breast cancer is closely linked to the estrogen receptor ERα, which is also considered to be an important target for the treatment of breast cancer. Therefore, compounds that can antagonize ERα activity may be drug candidates for the treatment of breast cancer. In drug development, to save manpower and resources, potential active compounds are often screened by establishing compound activity prediction model. For the 1974 compounds collected, the top 20 molecular descriptors that significantly affected the biological activity were screened using LASSO regression models combined with 10-fold cross-validation method. Further, a regression prediction model based on the MLP fully connected neural network was constructed to predict the bioactivity values of 50 new compounds. To measure the validity of the model, the model loss term was specified as the mean squared error (MSE). The results showed that the MLP-based regression prediction model had a loss value of 0.0146 on the validation set. This model is therefore well trained and the prediction strategy used is valid. The methods developed by this paper may provide a reference for the development of anti-breast cancer drugs.

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“…e feature subsets generated by FS methods for classification have two main goals, which are maximizing the classification accuracy (minimizing the classification error) and minimizing the number of selected features. As a mainstream classifier, K nearest neighbor (KNN) [56][57][58] is utilized for FS due to its advantages of simplicity and insensitivity to noisy data. Furthermore, how to reduce the number of selected features is considered another core issue.…”

Section: E Definition Of Objective Functionmentioning

confidence: 99%

Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of the Internet of Things (IoT), the curse of dimensionality becomes increasingly common. Feature selection (FS) is to eliminate irrelevant and redundant features in the datasets. Particle swarm optimization (PSO) is an efficient metaheuristic algorithm that has been successfully applied to obtain the optimal feature subset with essential information in an acceptable time. However, it is easy to fall into the local optima when dealing with high-dimensional datasets due to constant parameter values and insufficient population diversity. In the paper, an FS method is proposed by utilizing adaptive PSO with leadership learning (APSOLL). An adaptive updating strategy for parameters is used to replace the constant parameters, and the leadership learning strategy is utilized to provide valid population diversity. Experimental results on 10 UCI datasets show that APSOLL has better exploration and exploitation capabilities through comparison with PSO, grey wolf optimizer (GWO), Harris hawks optimization (HHO), flower pollination algorithm (FPA), salp swarm algorithm (SSA), linear PSO (LPSO), and hybrid PSO and differential evolution (HPSO-DE). Moreover, less than 8% of features in the original datasets are selected on average, and the feature subsets are more effective in most cases compared to those generated by 6 traditional FS methods (analysis of variance (ANOVA), Chi-Squared (CHI2), Pearson, Spearman, Kendall, and Mutual Information (MI)).

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Identifying DNA-binding proteins based on multi-features fusion and LASSO feature selection

Cited by 10 publications

References 0 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

MLP-Based Regression Prediction Model For Compound Bioactivity

Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning

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