Exploring a Secured Socket Python Flask Framework in Real Time Communication System

Uchenna, Uzo Izuchukwu; Gregory, Ugboaja Samuel; Virginus, Ugwu Nnaemeka; Angela, Obayi Adaora; Enyioma, Chigbundu Kanu; Ezeora, Nnamdi Johnson; Nneoma, Okwueze Chisom; Kenechukwu, Anigbogu; Michael, Ihedioha Uchechi

doi:10.9734/ajrcos/2021/v8i130194

Cited by 2 publications

(1 citation statement)

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“…The front end is developed using the Vue.js 3.0 [ 24 ] framework, which leverages reactivity and component-based architecture to deliver a highly interactive and user-friendly interface. For efficient data handling and separation of business logic, the back end is constructed using Python 3.9 [ 25 ], employing the Flask framework [ 26 ] to create a lightweight web service. Flask’s simplicity and flexibility enhance the speed and security of data transmission and processing.…”

Section: Resultsmentioning

confidence: 99%

BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs

Yuan,

Tang,

et al. 2024

Molecules

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m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications, a critical gap remains in the prediction of circRNA m6A modifications. Therefore, accurate identification and prediction of m6A sites are imperative for understanding RNA function and regulation. This study presents a novel hybrid model combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM) for precise m6A methylation site prediction in circular RNAs (circRNAs) based on data from HEK293 cells. This model exploits the synergy between CNN’s ability to extract intricate sequence features and BiLSTM’s strength in capturing long-range dependencies. Furthermore, the integrated attention mechanism empowers the model to pinpoint critical biological information for studying circRNA m6A methylation. Our model, exhibiting over 78% prediction accuracy on independent datasets, offers not only a valuable tool for scientific research but also a strong foundation for future biomedical applications. This work not only furthers our understanding of gene expression regulation but also opens new avenues for the exploration of circRNA methylation in biological research.

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Section: Resultsmentioning

confidence: 99%