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| http://dx.doi.org/10.1038/npp.2017.188 | DOI Listing |
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Distinct network patterns emerge from Cartesian and XOR epistasis models: a comparative network science analysis.
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School of Computing, Queen's University, 557 Goodwin Hall, 21-25 Union St, Kingston, K7L 2N8, Ontario, Canada.
Background: Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, significantly contributes to the phenotypic variance of complex traits. Traditionally, epistasis has been modeled using the Cartesian epistatic model, a multiplicative approach based on standard statistical regression. However, a recent study investigating epistasis in obesity-related traits has identified potential limitations of the Cartesian epistatic model, revealing that it likely only detects a fraction of the genetic interactions occurring in natural systems.
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Department of Computer Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran.
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Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
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Aptamers, single-stranded nucleic acids that bind to specific targets with high affinity and specificity, hold significant promise in various biomedical and biotechnological applications. The traditional method of aptamer selection, SELEX (Systematic Evolution of Ligands by EXponential Enrichment) takes a lot of work and time. Recent advancements in computational methods have revolutionized aptamer design, offering efficient and effective alternatives.
View Article and Find Full Text PDFRegboost: Enhancing mouse brain image registration using geometric priors and Laplacian interpolation.
Neuroimage
December 2024
Department of Computer Science, University of California, Irvine, CA 92617, USA. Electronic address:
We show in this work that incorporating geometric features and geometry processing algorithms for mouse brain image registration broadens the applicability of registration algorithms and improves the registration accuracy of existing methods. We introduce the preprocessing and postprocessing steps in our proposed framework as RegBoost. We develop a method to align the axis of 3D image stacks by detecting the central planes that pass symmetrically through the image volumes.
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