optimal transport

The paper introduces KCM, an optimization framework for protein and peptide design that outperforms generative models by enabling precise backbone geometry control with minimal computational resources.

This study introduces an optimization strategy for TMS-based motor mapping using farthest point sampling, showing it is more efficient and robust than random sampling.

This paper proposes optimal transport theory as a framework for modeling sub-letter orthographic processing, showing it aligns with early neural activity and captures geometric invariances better than existing methods.

The paper shows that selecting specific time points from fMRI data improves brain-behavior correlations compared to full scan data across multiple behaviors.

This paper develops a method to track microfluidic droplets over time using optimal transport and contrastive learning, achieving high accuracy in tracking without labeled data.

This paper introduces HM-OT to infer cell type differentiation maps from developmental transcriptomics time series by simultaneously clustering cells and learning transitions between cell types.

This paper introduces a method for optimizing the position and shape of deformable TMS coils based on individual head anatomy, improving electric field simulations and enabling systematic dose-response comparisons.

POASTA is an optimal algorithm for partial order alignment that improves speed and memory efficiency, enabling larger alignments in bacterial gene datasets.

This paper introduces an optimal transport-based method to aggregate residue-level protein language model embeddings into fixed-length representations, showing improved performance over average pooling for downstream tasks.

The paper develops a framework for designing perturbation signals to achieve desired information routing patterns in complex networks, applying it to brain stimulation and gene regulatory networks.

CORTADO identifies cell-type-specific marker genes using hill-climbing optimization, improving sensitivity and specificity over existing methods.

scEGOT is a single-cell trajectory inference framework that identifies cell populations and segregation events in time-series scRNA-seq data, revealing key transcription factors involved in PGCLC differentiation.

This paper introduces VeloVGI, a method for accurate RNA velocity estimation in batch scRNA-seq data, outperforming existing methods in various datasets.

This paper introduces a single-construct optogenetic tool to control talin dimerization, enabling precise manipulation of mechanotransduction in integrin-based adhesions.

This paper introduces RefCM, a method that improves cell type annotation in scRNA-seq data using optimal transport and integer programming, achieving accurate cross-technology, cross-tissue, and cross-species mappings.

This paper introduces a new method for genome-scale metabolic flux inference using conserved moieties, demonstrating its computational efficiency and accuracy with isotopic labeling data.

The paper develops a convex optimization framework for mate allocation in polyploid populations to control inbreeding and predict heterosis, showing improved genetic gain with pedigree kinship.

This paper uses conditional optimal transport to model CAR T cell responses at the single-cell level and predict gene expression for unseen CAR variants.

This paper introduces a method to construct pangenome graphs by solving an exact cover problem on blocks, showing how different objectives affect graph properties.

scMultiNODE integrates gene expression and chromatin accessibility data across timepoints to recover developmental dynamics and annotations for weak modalities.

The paper introduces CORNETO, a unified framework for knowledge-driven network inference from omics data, improving performance on biological tasks using simulated and real data.

This study uses optogenetics and mathematical modeling to show that the zinc-finger repressor Knirps rapidly and memorylessly represses gene expression by reducing transcriptional burst frequency.

This paper introduces Energy Aware Technology Mapping to design genetic logic circuits with energy efficiency and functionality, showing energy efficiency improves by 37.2% over functionally optimized variants.

The paper introduces a photoswitch called DEL-VPR that enables strong, light-induced gene expression in mammalian cells, surpassing constitutive promoters and improving protein production.

This paper develops a mathematical framework for optimal control of spiking neural networks, using a feedback controller to steer network dynamics towards target states.

This paper introduces Vina-GPU 2.1, an optimized version of AutoDock Vina that improves docking speed and precision using new algorithms and optimizations.

This paper optimizes combination immunotherapy regimens for glioblastoma using a virtual murine cohort and finds personalized treatment improves survival.

ImSpiRE enhances spatial transcriptomic resolution using image features and optimal transport to improve subspot-level resolution and discover tissue domains and signaling patterns.

The paper identifies alternate metabolic pathways rerouted during synthetic lethal reaction pairs using a constraint-based optimization approach and applies it to genome-scale models.

The paper examines how the BMP-Smad pathway balances trade-offs among response speed, noise amplification, and sensitivity to receptor input by tuning non-conserved parameters.