Cnnarginineme : A Cnn Structure for Training Models of Predicting Arginine Methylation Sites Based on the One-Hot Encoding of Peptide Sequence

Zhao, Jiaojiao; Zou, Guoyang; Xiao, Mingchao; Lin, Qian; Wang, Qiang; Liu, Jia; Ma, Leina

doi:10.2139/ssrn.4045843

Cited by 1 publication

(1 citation statement)

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“…Our approach has potential implications for both science and drug discovery. Predicting protein methylation sites can deliver insight into the biological functions of proteins and can be used to develop new treatments for diseases, including cancer and neurodegenerative diseases Zhao, Zou, Xiao, Lin, Wang, Liu and Ma (2022). The XAI method used in our study becomes more precise and interpretable by applying it to other computational biology and medicine machinelearning models.…”

Section: Introductionmentioning

confidence: 99%

RMSxAI: Arginine methylation sites prediction from protein sequencesusing Machine Learning algorithms and explainable ArtificialIntelligence

Dwivedi,

Khandelwal,

Rout

et al. 2023

Preprint

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Protein methylation is a vital regulator of many biological processes at the post-translational level,and accurate prediction of protein methylation sites is essential for research and drug discovery.In this paper, we present a new method, namely RMSxAI, to predict the arginine methylationsites from primary sequences using machine learning algorithms and describe the predictions usingexplainable artificial intelligence (XAI) techniques. Leveraging experimentally validated methylatedand unmethylated protein sequences from diverse organisms, we deduced several sequence features,encompassing physicochemical properties, amino acid composition, and evolutionary insights. Ourresults show that the proposed RMSxAI can predict protein methylation sites with high accuracy,bringing the F1 score up to 0.88 and overall accuracy up to 88.4%. We use various XAI methods toexplain the forecast. These include key features, partial occupancy maps, and local variation modelsthat provide insight into key features and interactions that lead to forecasts. Overall, our approach isrelevant to research and drug discovery, and our results demonstrate the potential of machine learningalgorithms and XAI methods to provide accurate and meaningful prediction of arginine methylationsites.

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Section: Introductionmentioning

confidence: 99%