Molecular characterization and genetic diversity of Babesia bovis and Babesia bigemina of cattle in Thailand

Srionrod, Nitipon; Nooroong, Pornpiroon; Poolsawat, Napassorn; Minsakorn, Sutthida; Watthanadirek, Amaya; Junsiri, Witchuta; Sangchuai, Siriphan; Chawengkirttikul, Runglawan; Anuracpreeda, Panat

doi:10.3389/fcimb.2022.1065963

Cited by 5 publications

(1 citation statement)

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“…An approximate sequence similarity of 65% among DENV serotypes has been demonstrated in multiple studies [33][34][35]. The exploration of motifs with statistical significance was integral in our 9 study of complete genome sequence data encompassing DENV 1 to 4 serotypes, juxtaposed with the generated sequences [36].…”

Section: Discussionmentioning

confidence: 89%

Deep Learning Guided Prediction Modeling of Dengue Virus Evolving Serotype

Mumtaz,

Shahbaz,

Qureshi

et al. 2024

Preprint

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Evolution remains an incessant process in viruses, allowing them to elude host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Predicting emerging viral genomes is crucial, particularly in diseases like dengue, where viruses disrupt host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging Dengue Virus (DENV) serotype. While Recurrent Neural Networks (RNN) were originally designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generative model showed accuracy of 93% and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score all to be around 99.00%, demonstrated the classification model’s reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99% similarity to DENV-4 and around 63-65% similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum 90% similarity underscored their uniqueness. We analyzed the conserved motifs in the genome through MEME Suite (version 5.5.5). Our research strives to contribute to the ongoing fight against the Dengue virus by offering predictive insights into its genomic evolution. Looking ahead, proactive predictive modeling before mutations occur holds potential for guiding vaccine design and diagnostic kit development.

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