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• W4385482783 abstract "Genomics sequencing has become more accessible thanks to advances in genomics technology. We have witnessed this, particularly in the COVID-19 pandemic with massive growth in viral (SARS-CoV-2) protein sequence data generation. Massive data, however, has been underutilized as a result of certain pandemic policies and countermeasures. For political or economic reasons, some wary countries have purposely created this issue, rather than being subject to the natural barriers to the availability of these data. All countries cannot be expected to actively contribute fair data to the scientific community; otherwise, their participation may become passive. We require a strategy to encourage nations across the globe to fairly exchange information on the pandemic situation and data. We propose a federated learning (FL)-based model to address the issue of data privacy and enable real-time surveillance of epidemics. In FL, we train a feed-forward neural network locally and transmit the updated weights to the central server to combine the learning from local data. In this way, a federated learning-based architecture offers data security because there is no need to share the data (the data does not leave the premises; just parameters or weights are communicated) and encourages all countries to contribute fairly to the research. While FL is popular in dealing with image data, in this paper we apply it to bioinformatics, in particular, protein sequence classification. The results show that the FL-based model not only performs better than the centralized, traditional deep learning models (such as CNN, GRU, LSTM, and feed-forward neural network) in terms of predictive accuracy and fairness in data utilization but that all of this can be accomplished while maintaining data privacy. Our findings imply that FL can address the problems of data availability and privacy without compromising results. Employing factual data and tracking the evolution and dissemination of new SARS-CoV-2 lineages in real-time, can aid in the disclosure of verifiable advancements in pandemic studies." @default.
• W4385482783 created "2023-08-03" @default.
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• W4385482783 date "2023-06-18" @default.
• W4385482783 modified "2023-10-11" @default.
• W4385482783 title "Empowering Pandemic Response with Federated Learning for Protein Sequence Data Analysis" @default.
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• W4385482783 doi "https://doi.org/10.1109/ijcnn54540.2023.10191721" @default.
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