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Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.
BMC Genomics
December 2024
College of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China.
Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes and are involved in complex human diseases through interactions with proteins. Accurate identification of lncRNA-protein interactions (LPI) can help elucidate the functional mechanisms of lncRNAs and provide scientific insights into the molecular mechanisms underlying related diseases. While many sequence-based methods have been developed to predict LPIs, efficiently extracting and effectively integrating potential feature information that reflects functional attributes from lncRNA and protein sequences remains a significant challenge.
View Article and Find Full Text PDFEDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance.
BMC Genomics
December 2024
School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
Background: The subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this.
View Article and Find Full Text PDFA randomised controlled pilot trial protocol for patient led cognitive gamified training during haemodialysis.
Sci Rep
December 2024
University of York, York, England, UK.
Disruptions in cognitive function have been reported in individuals undergoing haemodialysis and those with chronic kidney disease. This pilot study protocol primarily assesses the feasibility and acceptability of using mobile cognitive gaming apps for patient-led cognitive training during haemodialysis sessions. The protocol consists of three phases: (1) reviewing and evaluating available cognitive gaming apps, (2) conducting focus groups/interviews with people with kidney disease to determine app preferences, and (3) undertaking a quasi-experimental randomised controlled trial to compare cognitive outcomes between a patient-led app intervention group and a standard care control group over four months.
View Article and Find Full Text PDFAssociation between e-health literacy and perceived importance of future pandemic preparedness in sub-saharan Africa.
Sci Rep
December 2024
School of Nursing, Johns Hopkins University, Baltimore, MD, USA.
Introduction: Emerging and re-emerging infectious diseases continue to pose a severe threat to public health in Sub-Saharan Africa (SSA) and globally. Community-related interventions, such as community e-Health literacy, can contribute to the preparedness to respond effectively to emerging and re-emerging infectious diseases. This study investigated the relationship between e-Health literacy and SSA countries' perceptions of the importance of readiness for potential pandemics.
View Article and Find Full Text PDFPath planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm.
Sci Rep
December 2024
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China.
In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration-exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness.
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