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| http://dx.doi.org/10.3389/fgene.2014.00059 | DOI Listing |
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Acute diarrheal disease is one of the leading causes of death in children under age 5, disproportionately impacting children in low-resource settings. Many of these cases are caused by bacteria and therefore could respond to antibiotic treatment; however, the benefits of widely prescribing antibiotics must be weighed against the risks for the emergence of microbial resistance. These challenges present the opportunity for developing individualized treatment guidelines for diarrheal disease.
View Article and Find Full Text PDFAnalyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble.
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NASA Ames Research Center, Moffett Field, Mountain View, USA.
Spaceflight has several detrimental effects on human and rodent health. For example, liver dysfunction is a common phenotype observed in space-flown rodents, and this dysfunction is partially reflected in transcriptomic changes. Studies linking transcriptomics with liver dysfunction rely on tools which exploit correlation, but these tools make no attempt to disambiguate true correlations from spurious ones.
View Article and Find Full Text PDFRobust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.
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January 2025
Department of Physics, George Washington University, Washington, DC 20052, United States.
A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-distribution (OOD) generalization of deep learning (DL) models for single-sequence-based prediction of RNA secondary structure. The main idea behind this approach is to use DL models for in-distribution (ID) test sequences to leverage their superior ID performances, while relying on physics-based models for OOD sequences to ensure robust predictions. One key ingredient of the pipeline, named MoEFold2D, is automated ID/OOD detection via consensus analysis of an ensemble of DL model predictions without requiring access to training data during inference.
View Article and Find Full Text PDFIdentifying cancer prognosis genes through causal learning.
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School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, 130012 Changchun, China.
Accurate identification of causal genes for cancer prognosis is critical for estimating disease progression and guiding treatment interventions. In this study, we propose CPCG (Cancer Prognosis's Causal Gene), a two-stage framework identifying gene sets causally associated with patient prognosis across diverse cancer types using transcriptomic data. Initially, an ensemble approach models gene expression's impact on survival with parametric and semiparametric hazard models.
View Article and Find Full Text PDFSpatial transcriptomics data analysis integrates gene expression profiles with their corresponding spatial locations to identify spatial domains, infer cell-type dynamics, and detect gene expression patterns within tissues. However, the current spatial transcriptomics analysis neglects the multiscale cell-cell interactions that are crucial in biology. To fill this gap, we propose multiscale cell-cell interactive spatial transcriptomics (MCIST) analysis.
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