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HERMES is a molecular-formula-oriented and peak-detection-free method that uses LC/MS1 information to optimize MS2 acquisition for LC/MS-based metabolomic analysis.
The Signac framework enables the end-to-end analysis of single-cell chromatin data and interoperability with the Seurat package for multimodal analysis.
SpaGCN is a spatially resolved transcriptomics data analysis tool for identifying spatial domains and spatially variable genes using graph convolutional networks.
The NetID algorithm annotates untargeted LC-MS metabolomics data by combining known biochemical and metabolomic principles with a global network optimization strategy.
DeepLC, a deep learning-based peptide retention time predictor, can predict retention times for unmodified peptides as well as peptides with previously unseen modifications.
DeepFinder is a deep learning-based tool for identifying macromolecules in cellular cryo-electron tomograms. DeepFinder performs with an accuracy comparable to expert-supervised ground truth annotations on multiple experimental datasets.
DeepInterpolation is a self-supervised deep learning-based denoising approach for calcium imaging, electrophysiology and fMRI data. The approach increases the signal-to-noise ratio and allows extraction of more information from the processed data than from the raw data.
A compact adaptive optics module corrects aberrations in two-photon and three-photon microscopy, enabling structural and functional imaging deep in the mouse brain, the mouse spinal cord and the zebrafish larva.
An iSCAT image processing and analysis strategy enables mass-sensitive particle tracking (MSPT) of single unlabeled biomolecules on a supported lipid bilayer. MSPT was used to observe the (dis-)assembly of membrane complexes in real-time.
This work describes inCITE-seq that jointly measures intranuclear protein levels and the transcriptome in single nuclei, which is applied to mouse brain tissue to relate quantitative protein levels of TFs to gene expression programs.
Dynamic mass photometry allows label-free tracking and mass measurement of individual membrane-associated proteins diffusing on supported lipid bilayers. The approach can be used to monitor dynamic (dis)assembly of protein complexes.
By using a new deep learning architecture, Enformer leverages long-range information to improve prediction of gene expression on the basis of DNA sequence.
SEAM is a platform for the analysis of high-resolution secondary ion mass spectrometry imaging that allows spatially resolved nuclear metabolomic profiling at the single-cell level.
DeepImageJ offers a user-friendly solution in ImageJ to run trained deep learning models for biomedical image analysis. It includes guiding tools for reliable analyses, contributing to the democratization of deep learning in microscopy.