DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome.

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease.

Examining the Performance of Fog-Aided, Cloud-Centered IoT in a Real-World Environment.

The fog layer provides substantial benefits in cloud-based IoT applications because it can serve as an aggregation layer and it moves the computation resources nearer to the IoT devices; however, it is important to ensure adequate performance is achieved in such applications, as the devices usually communicate frequently and authenticate with the cloud. This can cause performance and availability issues, which can be dangerous in critical applications such as in the healthcare sector. In this paper, we analyze the efficacy of the fog layer in different architectures in a real-world environment by examining performance metrics for the cloud and fog layers using different numbers of IoT devices. We also implement the fog layer using two methods to determine whether different fog implementation frameworks can affect the performance. The results show that including a fog layer with semi-heavyweight computation capability results in higher capital costs, although the in the long run resources, time, and money are saved. This study can serve as a reference for fundamental fog computing concepts. It can also be used to walk practitioners through different implementation frameworks of fog-aided IoT and to show tradeoffs in order to inform when to use each implementation framework based on one's objectives.