iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC

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

doi:10.1016/j.ab.2018.04.021

Cited by 108 publications

(48 citation statements)

References 91 publications

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“…As shown in a series of recent publications (W. Chen, Ding, Zhou, Lin, & Chou, 2018;Cheng, Xiao, & Chou, 2018a, 2018bP. Feng et al, 2018;Jia, Li, Qiu, Xiao, & Chou, 2019;Khan, Rasool, Hussain, Khan, & Chou, 2018;Xiao, Xu, et al, 2018), to develop a really useful statistical predictor for a biological system, one should observe the guidelines of Chou's 5-step rule (Chou, 2011) to make the following five steps very clear: (i) how to construct or select a valid benchmark dataset to train and test the predictor; (ii) how to formulate the statistical samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted; (iii) how to introduce or develop a powerful algorithm (or engine) to operate the prediction; (iv) how to properly perform crossvalidation tests to objectively evaluate the anticipated accuracy of the predictor; (v) how to provide source code and dataset that are accessible to the public. Below, we are to describe how to deal with these steps one-by-one.…”

Section: Introductionmentioning

confidence: 90%

“…Furthermore, source codes and publicly accessible web-servers represent the current trend for developing various computational methods (W. Chen et al, 2018;Cheng et al, 2018aCheng et al, , 2018bChou et al, 2018;P. Feng et al, 2018;Jia et al, 2019;Khan et al, 2018;Xiao, Xu, et al, 2018). Actually, they have significantly enhanced the impacts of computational biology on medical science (Chou, 2015), driving medicinal chemistry into an unprecedented revolution (Chou, 2017), here we also publish our source codes and dataset at https://github.com/khanhlee/snare-cnn for presenting the new method reported in this paper.…”

Section: Comparative Performance For Identifying Snares Between 2d Cnmentioning

confidence: 99%

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Peer Review #2 of "SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data (v0.1)"

2019

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Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. This study attempts to use deep learning to predict SNARE proteins, which is one of the most vital molecular functions in life science. A functional loss of SNARE proteins has been implicated in a variety of human diseases (e.g., neurodegenerative, mental illness, cancer, and so on). Therefore, creating a precise model to identify their functions is a crucial problem for understanding these diseases, and designing the drug targets. Our SNARE-CNN model which uses two-dimensional convolutional neural networks and position-specific scoring matrices profiles could identify SNARE proteins with achieved sensitivity of 76.6%, specificity of 93.5%, accuracy of 89.7%, and MCC of 0.7 in cross-validation dataset. We also evaluate the performance of our model via an independent dataset and the result show that we are able to solve the overfitting problem. Compared with other state-of-theart methods, this approach achieved significant improvement in all of the metrics. Throughout the proposed study, we provide an effective model for identifying SNARE proteins and a basis for further research that can apply deep learning in bioinformatics, especially in protein function prediction. SNARE-CNN are freely available at https://github.com/khanhlee/snare-cnn .

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Section: Introductionmentioning

confidence: 90%

Section: Comparative Performance For Identifying Snares Between 2d Cnmentioning

confidence: 99%

Peer Review #2 of "SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data (v0.1)"

2019

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“…However, both the experimental and the biological ones can hardly meet the needs, which seem to be time-consuming and waste of resources in some degrees. Some PTM sites, including phosphorylation [22][23][24], S-nitrosylation [25][26][27][28], succinylation sites [29,30], hydroxylation sites [31,32], crotonylation [33,34], sumoylation [35], glycosylation [36], ubiquitination [37], prenylation [38], carbonylation [39], and methylation [40][41][42][43][44][45], have successfully been classified with the methods in silico. From these successful instances, we can easily find out that several key elements of such a classification issue should be pointed out.…”

Section: Complexitymentioning

confidence: 99%

LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree

et al. 2019

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Lysine lipoylation is a special type of posttranslational modification in both prokaryotes' and eukaryotes' proteomics researches. Such a modification takes part in several significant biological processions and plays a key role in the cellular level. In order to construct and design an accurate classification algorithm for identifying lipoylation sites in the protein level, the computational approaches should be taken into account in this field. Meanwhile, several factors plays different role in the identification of modification sites. Considering such a situation, the foundational elements of the effective identification of modification sites are the available feature description and the high effective classification. With these two elements, the distinguishing between the lipoylation samples and the nonlipoylation samples can be treated as a typical classification issue in the field of machine learning. In this work, we have proposed a method named LipoFNT, which employed the two featuring sets, including the Position-Specific Scoring Matrix and bi-profile Bayesian, as the classification features. And then, the flexible neural tree algorithm is utilized to deal with the imbalance classification issue in lipoylation modification sample dataset. The proposed method can achieve 81.07% in sn%, 80.29% in sp, 80.68% in Acc, 0.8076 in F1, and 0.6136 in MCC, respectively. Meanwhile, we have demonstrated the relationship between the lengths of peptide and identification of modification sites.

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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%

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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iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC

Cited by 108 publications

References 91 publications

Peer Review #2 of "SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data (v0.1)"

Peer Review #2 of "SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data (v0.1)"

LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree

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

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