iRecSpot-EF: Effective sequence based features for recombination hotspot prediction

Jani, Md. Rafsan; Mozlish, Toha Khan; Ahmed, Sajid; Tahniat, Niger Sultana; Farid, Dewan Md.; Shatabda, Swakkhar

doi:10.1016/j.compbiomed.2018.10.005

Cited by 21 publications

(17 citation statements)

References 31 publications

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“…To the best of our knowledge, the collection of these feature extractions and dimensionality reduction methods have not been presented in such an integrated toolkit, before. In addition, the conducted experimentation for all three DNA, RNA, and protein-based problems lead to results better than those reported in previous studies (Jani et al, 2018).…”

Section: Resultsmentioning

confidence: 61%

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences

et al. 2019

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Motivation Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs and RNAs. To build PyFeat we mainly focused on extracting features that capture information about the interaction of neighboring residues to be able to provide more local information. We then employ AdaBoost technique to select features with maximum discriminatory information. In this way, we can significantly reduce the number of extracted features and enable PyFeat to represent the combination of effective features from large neighboring residues. As a result, PyFeat is able to extract features from 13 different techniques and represent context free combination of effective features. The source code for PyFeat standalone toolkit and employed benchmarks with a comprehensive user manual explaining its system and workflow in a step by step manner are publicly available. Results https://github.com/mrzResearchArena/PyFeat/blob/master/RESULTS.md. Availability and implementation Toolkit, source code and manual to use PyFeat: https://github.com/mrzResearchArena/PyFeat/ Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 61%

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences

et al. 2019

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“…In this section, we derived a novel formula based on feature extract methods, such as GDC, RCC, PseTNC, and feature extracted in iRecSpot-EF (Jani et al, 2018 ) and their contributions. The novel formula can be written as Equation (10).…”

Section: Methodsmentioning

confidence: 99%

“…

In Equation (10) “a” and “λ” are two parameters having values between (0,1), which represent the contribution of a method in the feature vector. G represents GDC, R represents RCC, PseTNC represents PseTri-Nucleotide Composition and H represents 425 features generated by iRecSpot-EF (Jani et al, 2018 ). Further, G represents five selected features, R represents 12 selected features and PseTNC represents 66 features (Khan et al, 2019b ).…”

Section: Methodsmentioning

confidence: 99%

“…Zhang and Kong ( 2018a ) proposed iRSpot-ADPM using di-nucleotide composition (DNC) as a sequence formulation technique and SVM as a classification engine. Jani et al ( 2018 ) proposed iRecSpot-EF for the classification of recombination hotspots and coldspots. The authors employed K-mer for feature extraction, AdaBoost technique for feature selection, and logistic regression as classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

Khan

Iqbal

et al. 2020

Front. Genet.

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“…The proposed methodology has been simulated via different classification algorithms i.e., Multilayer Perceptron (MLP) [25], Support Vector Machines (SVM) [27], DT (Decision Tree), RF (Random Forest) [47][48] [49] and 2D-CNN. Deep learning attained huge and considerable attention concerning its implication in the field of computational genomics and proteomics [50]- [53].…”

Section: Model Architecturementioning

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

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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.

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iRecSpot-EF: Effective sequence based features for recombination hotspot prediction

Cited by 21 publications

References 31 publications

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences

Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

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

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iRecSpot-EF: Effective sequence based features for recombination hotspot prediction

Cited by 21 publications

References 31 publications

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences

Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

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

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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