optimal transport

This paper introduces a scalable optimization algorithm for solving the Beltway and Turnpike problems with uncertain measurements, showing improved accuracy and scalability on synthetic and partial digest data.

The paper introduces a framework for designing cell-type-specific promoters using model-based optimization, demonstrating improved specificity in blood cancer cell lines.

This paper introduces OptoTau, an optogenetic tool that induces different tau aggregation forms in Neuro2a cells based on blue light exposure patterns.

scTIE integrates temporal multimodal data and infers gene regulatory relationships predictive of cellular state changes, outperforming existing methods in data integration and biological signal preservation.

This paper develops a simulation-based optimization framework for designing genomic studies focused on clonal evolution, particularly in cancer, to optimize resource allocation and study design.

PILOT uses optimal transport to analyze single-cell and pathomics data, detecting disease-associated samples and non-linear changes in cell populations and tissue structures.

CellStitch improves 3D cell segmentation in anisotropic images using optimal transport, outperforming existing methods on anisotropic datasets.

This paper introduces a multiscale subspace identification algorithm for modeling multimodal spiking and field potential data, showing improved efficiency and accuracy in capturing low-dimensional dynamics and predicting behavior.

ROCCO identifies consensus open chromatin regions across multiple samples using convex optimization, improving upon existing peak calling methods.

This paper introduces BLU-VIPR, an optogenetic CRISPR tool that enables precise spatiotemporal gene editing in T lymphocytes in vivo by regulating gRNA production with blue light.

This paper uses optimal control theory to model morphogen-driven tissue patterning, showing how cells can achieve precise and reproducible fate decisions despite noise and dynamic feedback.

CINEMA-OT identifies causal effects of single-cell perturbations by separating confounding variation, outperforming existing methods on simulated and real datasets.

This study identifies a neural mechanism by which oxytocin recruits specific CeA neurons to switch fear responses to safety during social buffering in rats.

aKNNO improves clustering of both abundant and rare cell types in single-cell and spatial transcriptomics data by using an optimized adaptive k-nearest neighbor graph.

This paper applies trajectory optimization to drumming techniques to identify suboptimal strokes, find alternative techniques, analyze body type sensitivity, and create generalizable strategies for efficient drumming.

Mabs optimizes parameters of genome assemblers to improve gene assembly accuracy, outperforming default settings in six eukaryotic genomes.

This paper introduces a method to predict single-cell perturbation responses using neural optimal transport, showing improved performance over existing methods on drug-treated cells.

The paper introduces a matrix reordering algorithm called 'smooth-index' that outperforms existing methods in efficiency and scalability for neural circuit connectivity matrices.

This paper introduces a graph layout algorithm for pangenome graphs, enabling efficient visualization of large-scale genomic data.

This study uses optimal transport analysis on scRNA-seq data to reconstruct EMT trajectories in MCF10A cells, identifying three distinct fates and novel genes associated with different EMT states.

This paper introduces an unsupervised alignment method using Gromov-Wasserstein optimal transport for neuroscience, showing its ability to reveal structural distinctions in representations across different data domains.

This paper introduces BAGO, a Bayesian optimization method for optimizing LC gradients in untargeted LC-MS, improving compound annotation and spectral purity.

This paper introduces a novel optical design for axial de-scanning in light-sheet microscopy's detection arm, enabling aberration-free, multi-color volumetric imaging without sample translation.

This study uses optogenetics to show that RasGAPs regulate cell polarity by enhancing actomyosin contractility at the back and F-actin polymerization at the front.

The paper develops optimized matrix-vector multiplication methods for genomic prediction, improving computational efficiency on GPUs and CPUs for large datasets.

The paper develops scDOT, a method combining spatial transcriptomics and single-cell RNA-seq with optimal transport to improve spatial mapping of senescent cells and identify novel genes involved in cell-cell interactions.

PASTE2 aligns and integrates multi-slice spatial transcriptomics data with partial overlap, improving alignment accuracy and enabling 3D gene expression reconstruction.

SpikeShip identifies high-dimensional neural spiking patterns using optimal transport theory, distinguishing natural images and behavioral states in Neuropixel recordings.

This paper shows that re-implementing a traditional linear mixed model with a modern gradient solver can outperform deep learning models in speed and accuracy for biological data.

This paper introduces a differentiable method for inferring evolutionary trees from leaf nodes, achieving low error rates on up to 128 leaves.