A comprehensive tool for accurate identification of methyl-Glutamine sites

Malebary, Sharaf J.; Alzahrani, Ebraheem O.; Khan, Yaser Daanial

doi:10.1016/j.jmgm.2021.108074

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…This would be helping in developing more robust computational models that would assist in identification of m1A sites in an optimized way. To prevent the complete loss of the sequence-pattern information, Chou developed a pseudo-amino acid composition for proteins (PseAAC) [ 11 ]. Then pseudo-K-tuple nucleotide composition (PseKNC) was formulated as a result of the PseAAC success [ 12 , 13 ].…”

Section: Methodsmentioning

confidence: 99%

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m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

Suleman,

Alturise,

Alkhalifah

et al. 2024

BioData Mining

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Background 1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites. Objective Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated. Methodology The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models. Results The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics. Conclusion For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/.

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Section: Methodsmentioning

confidence: 99%

“…Similarly, another matrix, Ƚ PRIM [ 11 ], was formed for the tri-nucleotide base combination (i.e., AAA, AAG, AAU, …. CCG, CCU, CCC).…”

Section: Methodsmentioning

confidence: 99%

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

Suleman,

Alturise,

Alkhalifah

et al. 2024

BioData Mining

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“…All the above statistical functions, used for feature extraction, have specific biological significance. 33 – 44 These functions extract information related to the position and composition of DNA gene sequences. Feature extraction helps in extracting very useful information such as the frequency of each element in DNA gene sequences, position relative to the occurrence, composition of a specific gene, and absolute position of each element of gene sequences.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations

et al. 2022

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The abnormal growth of human healthy cells is called cancer. One of the major types of cancer is sarcoma, mostly found in human bones and soft tissue cells. It commonly occurs in children. According to a survey of the United States of America, there are more than 17,000 sarcoma patients registered each year which is 15% of all cancer cases. Recognition of cancer at its early stage saves many lives. The proposed study developed a framework for the early detection of human sarcoma cancer using deep learning Recurrent Neural Network (RNN) algorithms. The DNA of a human cell is made up of 25,000 to 30,000 genes. Each gene is represented by sequences of nucleotides. The nucleotides in a sequence of a driver gene can change which is termed as mutations. Some mutations can cause cancer. There are seven types of a gene whose mutation causes sarcoma cancer. The study uses the dataset which has been taken from more than 134 samples and includes 141 mutations in 8 driver genes. On these gene sequences RNN algorithms Long and Short-Term Memory (LSTM), Gated Recurrent Units and Bi-directional LSTM (Bi-LSTM) are used for training. Rigorous testing techniques such as Self-consistency testing, independent set testing, 10-fold cross-validation test are applied for the validation of results. These validation techniques yield several metrics such as Area Under the Curve (AUC), sensitivity, specificity, Mathew's correlation coefficient, loss, and accuracy. The proposed algorithm exhibits an accuracy of 99.6% with an AUC value of 1.00.

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“…PTMs are critical in maintaining protein structures [5] , functions [6] , metabolic regulation [7] , cellular signaling [8] , and proteomic diversity [9] , whereby our understanding of PTMs are essential to downstream consequences such as diseases. For example, S-nitrosylation is a promising therapeutic target for cancers and neurodegenerative diseases [10] , [11] , [12] ; methyl glutamine is associated with the host defence mechanism against microorganisms [13] , [14] . Different experimental techniques have been developed to reveal the mechanisms underlying PTMs, including chromatin immunoprecipitation (ChIP) [15] , western blotting (WB) [16] , mass spectrometry (MS) [17] , [18] , and isotope labeling [19] .…”

Section: Introductionmentioning

confidence: 99%

Mini-review: Recent advances in post-translational modification site prediction based on deep learning

Meng

Chan

Huang

et al. 2022

Computational and Structural Biotechnology Journal

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A comprehensive tool for accurate identification of methyl-Glutamine sites

Cited by 10 publications

References 44 publications

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations

Mini-review: Recent advances in post-translational modification site prediction based on deep learning

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