iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC

Khan, Yaser Daanial; Rasool, Nouman; Hussain, Waqar; Khan, Sher Afzal; Chou, Kuo‐Chen

doi:10.1007/s11033-018-4417-z

Cited by 59 publications

(29 citation statements)

References 74 publications

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“…The raw moments from the above equation are computed up to 3 rd order. These raw moments assume the origin of data as the reference point from where these moments are computed and the origin is utilized as the distance between the components [46]- [49]. The Raw moment unique features computed up to 3 rd order are labeled as…”

Section: Statistical Momentsmentioning

confidence: 99%

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CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences

Butt

Khan

2020

IEEE Access

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Cancerlectins are significantly important group of lectins that have an inhibitory effect on cancer cells with respect to their growth. They have a vital role in various tumor cell interactions like adhesion, growth, metastasis, differentiation and mainly in cellular infection. The investigations associated with cancerlectins are applicable to relevant studies in laboratories, diagnostics and therapy in clinical applications, and drug discoveries in targeting cancers. Prediction of cancerlectins is considered a helpful task due to the fact that they are specifically useful in dissecting cancers. Although, several Bioinformatics tools have been developed to predict cancerlectins, however, the need for improvement in the quality of its prediction model requires enhancements in the annotation and determination process of cancerlectins. In this study, a new model is proposed that builds on statistical moments based features to distinguish cancerlectins from non-cancerlectins. The currently proposed model achieved an accuracy of 88.36% using jackknife test which is better than current state-of-the-art models. These outcomes suggest that the use of statistical moments could bear more effective and efficient results. For the accessibility of the scientific community, a user-friendly web server has been developed which will associate the researchers in medical science. Web server is freely accessible at https://www.biopred.org/canlect. INDEX TERMS Cancerlectins, Hahn moments, lectins, moment invariants, PRIM.

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Section: Statistical Momentsmentioning

confidence: 99%

“…A data point from where all the data is uniformly distributed in all directions. These directions are relations of its weighted average [46], [49]- [51]. The central moments unique features computed up to 3 rd order, using the centroid of the data as their reference point, are computed from the following eqs.…”

Section: Statistical Momentsmentioning

confidence: 99%

CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences

Butt

Khan

2020

IEEE Access

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“…The number of scientists has been making their contributions [9][10][11][12] in order to 13 understand the cellular biological process and finding out medicines for cancer and for 14 various other diseases. The prediction of hydroxylation sites in the lab by using 15 method mass spectrometry [13] is difficult to conduct, expensive and very lengthy 16 process.…”

Section: Introductionmentioning

confidence: 99%

“…The above set of twenty feature vectors depends upon three properties of amino acids 136 such that, hydrophobicity, hydrophilicity and side chain mass of amino acids, can be 137 calculated by employing Eqs. (12) to (14). These equations can expand as per choice 138 of attributes of amino acids other than these three properties of amino acid.…”

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confidence: 99%

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iHyd-ProSite: A novel Computational Approach for Identifying Hydroxylation Sites in Proline Via Mathematical Modeling

Mahmood

2020

Preprint

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In various cellular functions, post translational modifications (PTM) of protein play a vital role. The addition of certain functional group through a covalent bond to the protein induces PTM. The number of PTMs are identified which are closely linked with diseases for example cancer and neurological disorder. Hydroxylation is one of the PTM, modified proline residue within a polypeptide sequence. The defective hydroxylation of proline causes absences of ascorbic acid in human which produce scurvy, and many other dominant health issues. Undoubtedly, the prediction of hydroxylation sites in proline residues is of challenging frontier. The experimental identification of hydroxyproline site is quite difficult, high-priced and time-consuming. The diversity in protein sequences instigates to develop a computational tool to identify hydroxylated site within short time with excellent prediction accuracy to handle such proteomics problems. In this work a novel in silico predictor is developed through rigorous mathematical modeling to identify which site of proline is hydroxylated and which site is not? Then performance of the predictor was verified using three validations tests, namely self-consistency test, cross-validation test and jackknife test over the benchmark dataset. A comparison was established for jackknife test with the previous methods. In comparison with previous predictors the proposed tool is more accurate than the existing techniques. Hence this scheme is highly useful and inspiring in contrast to all previous predictors. 10

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Computer Molecular Biology Technology in the Field of Clinical Microbiological Examination

Liu

Sun

Dong

2021

2021 International Conference on Applications and Techniques in Cyber Intelligence

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iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC

Cited by 59 publications

References 74 publications

CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences

CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences

iHyd-ProSite: A novel Computational Approach for Identifying Hydroxylation Sites in Proline Via Mathematical Modeling

Computer Molecular Biology Technology in the Field of Clinical Microbiological Examination

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