optimal transport

CMOT infers missing single-cell modalities using optimal transport, outperforming existing methods and producing biologically interpretable results.

This paper develops a method to evaluate energy yield and driving force in microbial metabolic pathways using thermodynamic principles and multi-objective optimization, applied to propionate oxidation and reverse TCA cycle.

This paper develops a method to automatically design optimal experimental protocols for calibrating cardiac electrophysiology models to better identify cell-specific parameters.

This study introduces a gene filter denoising algorithm using optimal transport to reduce noise in spatially resolved transcriptomics data, improving clustering and cell type annotation.

This study uses single-cell spatial transcriptomics and optimal transport analysis to reveal therapy-induced changes in pancreatic cancer microenvironment interactions.

The paper compares spike-rate and spike-timing codes in visual cortex during natural movie viewing, finding that temporal spiking sequences carry more information and are more stable than spike-rate vectors.

This study uses optimal control simulations to show that transfemoral limb loss modestly increases walking metabolic cost, but large discrepancies in gait analysis may be avoidable.

ISRES+ improves parameter estimation in systems biology models by enhancing an evolutionary strategy to better minimize error functions.

AddBiomechanics automates biomechanical analysis of human motion data, reducing processing time from days to minutes and achieving accuracy comparable to manual methods.

The paper introduces JSIT, an optimization-based method for decoding imaging spatial transcriptomics data with lower magnification, improving throughput and recovery performance over standard methods.

The paper introduces an optogenetic system to control transcriptional modulators using light, enabling precise modulation of cell fate and signaling pathways.

The paper introduces a method to track neurons across multiple in-vivo imaging sessions without requiring full field-of-view registration, validated on simulated and experimental datasets.

This paper presents an optimization-based method to design synthetic gene circuits with specified bifurcation diagrams, focusing on mushroom bifurcation for applications in biosensors and synthetic biology.

RIBAP improves pangenome analysis by using ILP to refine gene clusters, outperforming existing tools in identifying core genes across diverse bacterial genomes.

This paper optimises asymmetric field pulses for transcranial magnetic stimulation to reduce energy loss and coil heating, showing that asymmetric pulses with specific waveform characteristics are more power-efficient than conventional ones.

The paper introduces a Bayesian optimization pipeline for demographic inference, showing it outperforms genetic algorithms for larger population counts.

This paper uses optimal control theory on a menstrual cycle model to determine minimal hormone doses and timing for contraception, achieving up to 92% reduction in estrogen monotherapy.

This paper introduces a sparse and low-rank Poisson unmixing method to improve fluorescence image unmixing accuracy in low signal-to-noise regimes with overlapping fluorophores.

This paper extends the CAROT method to stack multiple optimal transport mappings between atlases, improving efficiency while maintaining performance in connectome reconstruction.

OSCAR uses an L0-pseudonorm approach to select sparse prognostic features in prostate cancer, balancing model accuracy and clinical implementation costs.

Lpnet reconstructs phylogenetic networks from distances using integer linear programming, improving approximation accuracy over Neighbor-net for most datasets.

TACCO is a framework for annotating cell identities in single-cell and spatial omics data, excelling in speed and performance across datasets.

This paper introduces a physical neural mass model framework to infer oscillatory generator locations from laminar electrophysiological recordings in macaque monkeys.

The paper introduces an efficient algorithm for computing Nash equilibria in multispecies ecological models to improve fisheries management.

This paper introduces COMMOT, a method for inferring cell-cell communication in spatial transcriptomics, and demonstrates its effectiveness across multiple datasets and technologies.

This paper develops a framework called PASCAR to systematically explore CAR-T cell design space using multiscale models and optimization, showing intermediate CAR affinities reduce healthy cell lysis while maintaining tumor cell killing.

This paper presents a method to register light sheet microscopy data with magnetic resonance histology to restore in-skull morphology, achieving accurate registration with low displacement error.

The paper develops a method to restrict the search space for PROTAC-mediated protein-protein interactions using a cyclic coordinate descent algorithm, showing the global minimum corresponds to native-like conformations.

This paper introduces a green-light activated CRISPRi system with a 40-fold dynamic range for precise gene repression in E. coli.

This paper develops a data-driven framework for controlling neural systems using optimal control theory and deep learning, demonstrating its ability to restore function in simulated neural systems under perturbation.