DBPPred-PDSD: Machine learning approach for prediction of DNA-binding proteins using Discrete Wavelet Transform and optimized integrated features space

Ali, Farman; Kabir, Muhammad; Arif, Muhammad; Swati, Zar Nawab Khan; Khan, Zaheer; Ullah, Matee; Yu, Dong‐Jun

doi:10.1016/j.chemolab.2018.08.013

Cited by 60 publications

(31 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Use of PDB based DBP and control data will also allow us to make a broad comparison with other methods of DBP predictions that have been previously published and where data sets are in public domain. In this regards, most recently Ali et al have used two standard datasets namely PDB1075 as Benchmark dataset and PDB186 as Independent dataset for DBP prediction and these and similar data sets have been widely used for the development of DBP prediction methods, These Benchmark datasets were originally developed by Liu et al, who first extracted the DBPs from updated Protein Database (PDB) . Second, they eliminated protein sequences with unknown and length less than 50 amino acids.…”

Section: Resultsmentioning

confidence: 99%

“…Second, they eliminated protein sequences with unknown and length less than 50 amino acids. Ali et al have also introduced some other protein sequence elimination criteria that causes incorrect design of a predictive model effecting classification of the uncharacterized protein sequences. We have trained CNN and MLP architecture as shown in Figures and , on the Benchmark dataset using jackknife cross validation scheme.…”

Section: Resultsmentioning

confidence: 99%

“…We compared our experimental results with prior methods including DNA‐Prot, iDNA‐Prot, iDNA‐Protdis, DBPPred, Kmerl + ACC, Local‐DPP, PSFM‐DBT, HMMBinder, and DBPPred‐PDSD in the literature for both Benchmark and Independent datasets. The results are noted in Table , which are taken from their respective articles.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence

Chauhan

Ahmad

2019

Proteins

View full text Add to dashboard Cite

Sequence based DNA‐binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA‐binding residues well on known DBPs but the same models cannot be applied to unequally sized protein sequences. PSSM summaries representing column averages and their amino‐acid wise versions have been effectively used for the task, but it remains unclear if these features carry all the PSSM's predictive power, traditionally harnessed for binding site predictions. Here we evaluate if PSSMs scaled up to a fixed size by zero‐vector padding (pPSSM) could perform better than the summary based features on similar models. Using multilayer perceptron (MLP) and deep convolutional neural network (CNN), we found that (a) Summary features work well for single‐genome (human‐only) data but are outperformed by pPSSM for diverse PDB‐derived data sets, suggesting greater summary‐level redundancy in the former, (b) even when summary features work comparably well with pPSSM, a consensus on the two outperforms both of them (c) CNN models comprehensively outperform their corresponding MLP models and (d) actual predicted scores from different models depend on the choice of input feature sets used whereas overall performance levels are model‐dependent in which CNN leads the accuracy.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence

Chauhan

Ahmad

2019

Proteins

View full text Add to dashboard Cite

show abstract

“…Constructing a reliable benchmark dataset could guarantee the reliability of the proposed computational model [22]- [27]. In this work, samples were gained from previous studies of [3], [20] studies [3], [20], which were rigorously screened through the following three steps:…”

Section: Benchmark Datasetmentioning

confidence: 99%

“…To remove and make the feature space optimally fit for the prediction of unseen data an excellent feature selection strategy is employed. As it is evident from several research studies [24], [27], [32]- [34], that feature selection plays a prominent role in building a reliable computational model. The optimum selected feature prevents the model from the curse of dimensionality, avoids overfitting, reduces training time, and enhanced model generalizability.…”

Section: Feature Selectionmentioning

confidence: 99%

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique

Wang¹,

Khan

Ali

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundBacteriophage or phage is a type of virus that replicates itself inside bacteria. It consist of genetic material surrounded by a protein structure. Bacteriophage plays a vital role in the domain of phage therapy and genetic engineering. Phage and hydrolases enzyme proteins have a significant impact on the cure of pathogenic bacterial infections and disease treatment. Accurate identification of bacteriophage proteins is important in the host subcellular localization for further understanding of the interaction between phage, hydrolases, and in designing antibacterial drugs. Looking at the significance of Bacteriophage proteins, besides wet laboratory-based methods several computational models have been developed so far. However, the performance was not considerable due to inefficient feature schemes, redundancy, noise, and lack of an intelligent learning engine. Therefore we have developed an anovative bi-layered model name DeepEnzyPred. A Hybrid feature vector was obtained via a novel Multi-Level Multi-Threshold subset feature selection (MLMT-SFS) algorithm. A two-dimensional convolutional neural network was adopted as a baseline classifier.ResultsA conductive hybrid feature was obtained via a serial combination of CTD and KSAACGP features. The optimum feature was selected via a Novel Multi-Level Multi-Threshold Subset Feature selection algorithm. Over 5-fold jackknife cross-validation, an accuracy of 91.6 %, Sensitivity of 63.39%, Specificity 95.72%, MCC of 0.6049, and ROC value of 0.8772 over Layer-1 were recorded respectively. Similarly, the underline model obtained an Accuracy of 96.05%, Sensitivity of 96.22%, Specificity of 95.91%, MCC of 0.9219, and ROC value of 0.9899 over layer-2 respectivily.ConclusionThis paper presents a robust and effective classification model was developed for bacteriophage and their types. Primitive features were extracted via CTD and KSAACGP. A novel method (MLMT-SFS ) was devised for yielding optimum hybrid feature space out of primitive features. The result drew over hybrid feature space and 2D-CNN shown an excellent classification. Based on the recorded results, we believe that the developed predictor will be a valuable resource for large scale discrimination of unknown Phage and hydrolase enzymes in particular and new antibacterial drug design in pharmaceutical companies in general.

show abstract

DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information

Ali

Ahmad

Swati

et al. 2019

J Comput Aided Mol Des

View full text Add to dashboard Cite

DBPPred-PDSD: Machine learning approach for prediction of DNA-binding proteins using Discrete Wavelet Transform and optimized integrated features space

Cited by 60 publications

References 74 publications

Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence

Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique

DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information

Contact Info

Product

Resources

About

DBPPred-PDSD: Machine learning approach for prediction of DNA-binding proteins using Discrete Wavelet Transform and optimized integrated features space

Cited by 60 publications

References 74 publications

Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence

Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their&nbsp;Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique

DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information

Contact Info

Product

Resources

About

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique