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By probabilistic modeling of gene regulation and expression kinetics, Dictys infers dynamic and context-specific gene regulatory networks using single-cell multiomics data.
This study shows the importance of proper metrics for comparing algorithms for bioimage segmentation and object detection by exploring the impact of metrics on the relative performance of algorithms in three image analysis competitions.
DBlink uses deep learning to capture long-term dependencies between different frames in single-molecule localization microscopy data, yielding super spatiotemporal resolution videos of fast dynamic processes in living cells.
Open-3DSIM is a versatile open-source software for high-fidelity reconstruction of three-dimensional structured illumination microscopy data (with polarization). It is available in three convenient forms for user-friendly and customizable applications.
Red and green genetically encoded indicators for norepinephrine have been developed and employed to monitor norepinephrine during locomotion and reward behavior in mice. The strategy used for generating these indicators also produced indicators for other neuromodulators.
Voltage-Seq combines voltage imaging, optogenetics and single-cell RNA-seq for high-throughput analysis of functional and transcriptomic properties of neurons in situ.
This study describes benchmarking and validation of computational tools for detecting circRNAs, finding most to be highly precise with variations in sensitivity and total detection. The study also finds over 315,000 putative human circRNAs.
This study presents a significance analysis framework for evaluating single-cell clusters. Application of the method detects cases of over-clustering in reported single-cell RNA-sequencing analysis results.
A combination of gentle stimulated emission depletion microscopy imaging and deep-learning-based improvements in signal-to-noise ratio enables high-resolution reconstruction of neuronal architecture in living tissue.
Subcellular spatial transcriptomics cell segmentation (SCS) combines information from stained images and sequencing data to improve cell segmentation in high-resolution spatial transcriptomics data.
The ASAP4 family of genetically encoded voltage indicators allows recording of action potentials and subthreshold activity with either one- or two-photon microscopy over extended periods of time.
This study shows, when analyzing multi-sample metagenomic datasets, the multi-coverage binning approach outperforms the single-coverage binning alternative in generating bins with higher quality and less contamination.
By learning a joint representation using deep generative modeling, MultiVI integrates multimodal and single-modality single-cell datasets, which enhances multiple functionalities.
CherryML is a method to scale up maximum likelihood estimation for general phylogenetic models of molecular evolution, providing several orders of magnitude speedup over traditional methods.
The PanGenome Research Tool Kit (PGR-TK) achieves flexible and scalable representation, visualization and analysis of genomic variation using pangenome graphs.