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The Venus score for the assessment of the quality and trustworthiness of biomedical datasets.
BioData Min
January 2025
Fondazione Bruno Kessler, Trento, Italy.
Biomedical datasets are the mainstays of computational biology and health informatics projects, and can be found on multiple data platforms online or obtained from wet-lab biologists and physicians. The quality and the trustworthiness of these datasets, however, can sometimes be poor, producing bad results in turn, which can harm patients and data subjects. To address this problem, policy-makers, researchers, and consortia have proposed diverse regulations, guidelines, and scores to assess the quality and increase the reliability of datasets.
View Article and Find Full Text PDFLong-range trajectory reconstructions using the point mass model.
J Forensic Sci
January 2025
Netherlands Forensic Institute, Den Haag, Netherlands.
In shooting incident reconstructions, forensic examiners usually deal with scenes involving short-range trajectories, typically ≤30 m. In situations such as this, a linear trajectory reconstruction model is appropriate. However, a forensic expert can also be asked to estimate a shooter's position by reconstructing a long-range trajectory where the bullet's path becomes arced as a result of gravity and the greater time in flight.
View Article and Find Full Text PDFAddressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL.
Brief Bioinform
November 2024
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui, China.
Despite significant advancements in single-cell representation learning, scalability and managing sparsity and dropout events continue to challenge the field as scRNA-seq datasets expand. While current computational tools struggle to maintain both efficiency and accuracy, the accurate connection of these dropout events to specific biological functions usually requires additional, complex experiments, often hampered by potential inaccuracies in cell-type annotation. To tackle these challenges, the Zero-Inflated Graph Attention Collaborative Learning (ZIGACL) method has been developed.
View Article and Find Full Text PDFAn efficient smart phone application for wheat crop diseases detection using advanced machine learning.
PLoS One
January 2025
Department of Computer Science, National Textile University, Faisalabad, Pakistan.
Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges from various diseases. Timely and accurate identification of these wheat plant diseases is crucial to mitigate damage and enhance overall yield.
View Article and Find Full Text PDFDFAST_QC: quality assessment and taxonomic identification tool for prokaryotic Genomes.
BMC Bioinformatics
January 2025
Department of Genetics, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Mishima, 411-8540, Japan.
Background: Accurate taxonomic classification in genome databases is essential for reliable biological research and effective data sharing. Mislabeling or inaccuracies in genome annotations can lead to incorrect scientific conclusions and hinder the reproducibility of research findings. Despite advances in genome analysis techniques, challenges persist in ensuring precise and reliable taxonomic assignments.
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