iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-steps Rule

Ilyas, Sarah; Hussain, Waqar; Ashraf, Adeel; Khan, Yaser Daanial; Khan, Sher Afzal; Chou, Kuo Chen

doi:10.2174/1389202920666190809095206

Cited by 45 publications

(11 citation statements)

References 170 publications

(144 reference statements)

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“…60% cutoff depicts that all sequences, which showed similarity more than 60% were excluded from the dataset to reduce redundancy in dataset and overfitting of the model. The reason for choosing this threshold was that it is supported by various previously reported studies (Shao and Chou 2020 ; Shao et al 2020 ; Khan et al 2019a , b ; Jia et al 2019 ; Ilyas et al 2019 ; Hussain et al 2019a , b ; Feng et al 2019 ; Cui et al 2019 ; Awais et al 2019 ). Clusters were formed comprising of 5101 non-redundant DNA replication proteins and 5227 non-redundant non-DNA replication proteins.…”

Section: Methodsmentioning

confidence: 90%

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

Amin

Awais

Sahai

et al. 2021

Int J Pept Res Ther

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DNA replication is one of the specific processes to be considered in all the living organisms, specifically eukaryotes. The prevalence of DNA replication is significant for an evolutionary transition at the beginning of life. DNA replication proteins are those proteins which support the process of replication and are also reported to be important in drug design and discovery. This information depicts that DNA replication proteins have a very important role in human bodies, however, to study their mechanism, their identification is necessary. Thus, it is a very important task but, in any case, an experimental identification is time-consuming, highly-costly and laborious. To cope with this issue, a computational methodology is required for prediction of these proteins, however, no prior method exists. This study comprehends the construction of novel prediction model to serve the proposed purpose. The prediction model is developed based on the artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. Highest overall accuracy has been achieved through tenfold cross-validation and Jackknife testing that was computed to be 96.22% and 98.56%, respectively. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a time and cost-effective stratagem for designing novel drugs to strike the contemporary bacterial infection.

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Section: Methodsmentioning

confidence: 90%

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

Amin

Awais

Sahai

et al. 2021

Int J Pept Res Ther

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“…Further, statistical moments are computed from the obtained square matrix for dimensionality reduction and forming fixed-size feature vectors 27 , 28 . As discussed previously, the three moments deployed in this study are Hahn, central and raw moments.…”

Section: Methodsmentioning

confidence: 99%

Evaluating machine learning methodologies for identification of cancer driver genes

Malebary

Khan

2021

Sci Rep

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Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew’s correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.

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“…Thus, to represent sequences as vectors, the pseudo amino acid composition (PseAAC) was proposed 17 . The idea of PseAAC is popular in bioinformatics research 18 , 19 and has been used in numerous bio-medicine and medication improvement studies 20 , 21 as well as other disciplines of computational proteomics. An extensive rundown of references is provided in a survey paper 22 .…”

Section: Methodsmentioning

confidence: 99%

Identification of stress response proteins through fusion of machine learning models and statistical paradigms

Alzahrani

Alghamdi

Ullah

et al. 2021

Sci Rep

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Proteins are a vital component of cells that perform physiological functions to ensure smooth operations of bodily functions. Identification of a protein's function involves a detailed understanding of the structure of proteins. Stress proteins are essential mediators of several responses to cellular stress and are categorized based on their structural characteristics. These proteins are found to be conserved across many eukaryotic and prokaryotic linkages and demonstrate varied crucial functional activities inside a cell. The in-vivo, ex vivo, and in-vitro identification of stress proteins are a time-consuming and costly task. This study is aimed at the identification of stress protein sequences with the aid of mathematical modelling and machine learning methods to supplement the aforementioned wet lab methods. The model developed using Random Forest showed remarkable results with 91.1% accuracy while models based on neural network and support vector machine showed 87.7% and 47.0% accuracy, respectively. Based on evaluation results it was concluded that random-forest based classifier surpassed all other predictors and is suitable for use in practical applications for the identification of stress proteins. Live web server is available at http://biopred.org/stressprotiens, while the webserver code available is at https://github.com/abdullah5naveed/SRP_WebServer.git

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iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-steps Rule

Cited by 45 publications

References 170 publications

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

Evaluating machine learning methodologies for identification of cancer driver genes

Identification of stress response proteins through fusion of machine learning models and statistical paradigms

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