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Batch effects pose great statistical challenges to the analysis of biomedical data. One approach to address batch effects is through sample remeasurement in each batch. In this work, the researchers developed a rigorous batch-effect correction procedure based on remeasured samples.
Graph neural networks (GNNs) present a promising route for machine learning of solid-state materials’ properties, but methods capable of directly predicting defect properties from ideal, defect-free structures are needed. A GNN developed for direct defect property predictions enables high-throughput screening of redox-active oxides for energy applications and beyond.
The dissipation and bending of light waves by atmospheric turbulence adversely affects infrared imaging, leading to grayscale drift, distortion, and blurring. A deep learning method has been developed to both extract the two-dimensional atmospheric turbulence strength fields and obtain clear and stable images from turbulence-distorted infrared images.
Automating materials’ defect predictions with graph neural networks, when coupled to first principles thermodynamic calculations, accelerates materials discovery for a variety of high-temperature, clean-energy applications.
This study introduces a physically boosted cooperative learning framework (PBCL) to reveal 2D atmospheric turbulence strength fields from turbulence-distorted infrared images. The PBCL is further demonstrated to inhibit adverse turbulence effects in images.
A system called ORFanage can analyze RNA-seq data to find novel protein variants and improve gene annotations. In addition, the method is fast and scalable, being able to filter out noise and thus greatly improving the quality of transcriptome assemblies.
Social media and other internet platforms are making it even harder for researchers to investigate their effects on society. One way forward is user-sourced data collection of data to be shared among many researchers, using robust ethics tools to protect the interests of research participants and society.
A hierarchical Bayesian method identifies cell-type specific changes in gene regulatory circuits in disease by integrating single-cell and three-dimensional measurements of the genome.
While the increasing availability of data creates unprecedented research opportunities, it is important to understand the provenance of these datasets to ensure reliable data-driven conclusions.
A Bayesian method, called MAGICAL, that contrasts single cell multiomics data across conditions to accurately discover differences in gene regulatory circuits at cell type resolution is applied to specific host-based diagnosis of bacterial sepsis.
While the adherence to fairness constraints has become common practice in the design of algorithms across many contexts, a more holistic approach should be taken to avoid inflicting additional burdens on individuals in all groups, including those in marginalized communities.
A recent work introduces a cellular deconvolution method, MeDuSA, of estimating cell-state abundance along a one-dimensional trajectory from bulk RNA-seq data with fine resolution and high accuracy, enabling the characterization of cell-state transition in various biological processes.