Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Rout, Ranjeet Kumar; Umer, Saiyed; Khandelwal, Monika; Pati, Smitarani; Mallik, Saurav; Balabantaray, Bunil Kumar; Qin, Hong

doi:10.3389/fgene.2023.1154120

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…Chen et al combined Z-curve pseudo-k-tuple nucleotide composition with an SVM classifier to construct a model aimed at capturing DNA sequence patterns associated with essential genes [ 33 ]. In addition to these methods, Le et al utilized natural language processing methods to comprehend DNA sequence features associated with gene essentiality and integrated deep neural networks to predict these essential genes [ 34 ], Rout et al conducted feature counting, including parameters such as energy, entropy, uniformity, and contrast within nucleotides [ 35 ], while simultaneously employing supervised machine learning methods for identification, among other techniques.Overall, there is a growing body of research utilizing machine learning methods for essential gene prediction [ 36 , 37 ], which has led to significant improvements in prediction performance. However, Most machine learning methods for predicting essential genes rely on their protein sequence data.…”

Section: Introductionmentioning

confidence: 99%

Essential genes identification model based on sequence feature map and graph convolutional neural network

Hu,

Li,

Xiao

et al. 2024

BMC Genomics

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Background Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes. Results In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training. Conclusions Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research.

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Section: Introductionmentioning

confidence: 99%

Essential genes identification model based on sequence feature map and graph convolutional neural network

Hu,

Li,

Xiao

et al. 2024

BMC Genomics

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Protein feature engineering framework for AMPylation site prediction

Prabhu,

Bhosale,

Sane

et al. 2024

Sci Rep

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AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model’s capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations.

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DeepPRMS: advanced deep learning model to predict protein arginine methylation sites

Khandelwal,

Kumar Rout

2024

Briefings in Functional Genomics

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Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research and drug discovery. Some experimental techniques, such as methyl-specific antibodies, chromatin immune precipitation and mass spectrometry, exist for predicting protein methylation sites, but these techniques are time-consuming and costly. The ability to predict methylation sites using in silico techniques may help researchers identify potential candidate sites for future examination and make it easier to carry out site-specific investigations and downstream characterizations. In this research, we proposed a novel deep learning-based predictor, named DeepPRMS, to identify protein methylation sites in primary sequences. The DeepPRMS utilizes the gated recurrent unit (GRU) and convolutional neural network (CNN) algorithms to extract the sequential and spatial information from the primary sequences. GRU is used to extract sequential information, while CNN is used for spatial information. We combined the latent representation of GRU and CNN models to have a better interaction among them. Based on the independent test data set, DeepPRMS obtained an accuracy of 85.32%, a specificity of 84.94%, Matthew’s correlation coefficient of 0.71 and a sensitivity of 85.80%. The results indicate that DeepPRMS can predict protein methylation sites with high accuracy and outperform the state-of-the-art models. The DeepPRMS is expected to effectively guide future research experiments for identifying potential methylated protein sites. The web server is available at http://deepprms.nitsri.ac.in/.

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Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Cited by 3 publications

References 44 publications

Essential genes identification model based on sequence feature map and graph convolutional neural network

Essential genes identification model based on sequence feature map and graph convolutional neural network

Protein feature engineering framework for AMPylation site prediction

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites

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