optimal transport

OpenGRF: Predicting Ground Reaction Forces and Moments During Daily Living Activities in OpenSim

OpenGRF estimates ground reaction forces and moments from kinematics in OpenSim, achieving high accuracy for vertical forces and ankle moments but lower accuracy for medio-lateral forces and free moments.

Designing proteins with reduced T-cell epitopes through policy optimization

The paper designs proteins with reduced T-cell epitopes using deep generative models and policy optimization to improve immune compatibility across MHC Class I alleles.

Inferring the Metabolic Objectives of Mammalian Cells via Inverse Modeling of Fluxomics and Metabolomics

This paper introduces ObjFind-M, an inverse optimization framework that infers reaction-level metabolic objectives from fluxomic and metabolomic data in mammalian cells, finding mitochondrial ATP synthase as a central driver.

OT-RMSD: A Generalization of the Root-Mean-Square Deviation for Aligning Unequal-Length Protein Structures

OT-RMSD generalizes the RMSD metric for aligning unequal-length protein structures without pre-defined alignment, showing improved accuracy over existing methods.

OTMalign: A Fast and Robust TM-score-driven Protein Structure Alignment Algorithm using Optimal Transport

OTMalign optimizes TM-score for protein structure alignment using optimal transport, achieving fast and high-quality results on benchmark datasets.

LieOTMap-FFT: A Differentiable Fitting Framework Combining Lie Algebra and FFT-accelerated Optimal Transport for Cryo-EM Maps

LieOTMap-FFT aligns cryo-EM maps with atomic models using Lie algebra and FFT-accelerated optimal transport, achieving high accuracy in rigid body fitting.

LieOTMap: A Differentiable Approach to Cryo-EM Fitting via Lie-Theoretic Optimal Transport

LieOTMap fits high-resolution atomic models into cryo-EM maps using a differentiable framework based on Lie algebra and optimal transport, achieving accurate alignment of E. coli GroEL.

LieMap: A Fast and Transparent Gradient-Based Method for Cryo-EM Structure Fitting

LieMap fits high-resolution atomic models into cryo-EM maps using gradient-based optimization, achieving a low RMSD for E. coli GroEL structure fitting.

Integrating gene drives with established pest controls to mitigate spillover risk

This study shows integrating gene drives with pest controls can reduce spillover risk by optimizing treatment strategies based on population dynamics and cost-effectiveness.

LieOTDock: A Protein Docking Framework via Lie Group Optimization and Optimal Transport

LieOTDock generates high-quality protein docking poses using Lie group optimization and optimal transport, achieving 3.43 Å RMSD on a benchmark in under three minutes.

LieOTAlign: A Differentiable Protein Structure Alignment Framework Combining Optimal Transport and Lie Algebra

LieOTAlign introduces a differentiable protein structure alignment framework using Lie algebra and optimal transport, achieving higher TM-scores on benchmark datasets.

An optimal transport-based low-dimensional visualization framework for high-parameter flow cytometry

This paper introduces an optimal transport-based framework for visualizing high-parameter flow cytometry data, providing biologically consistent layouts and enabling quantitative analysis of population changes.

Optimization-based dietary recommendations for healthy eating

This paper develops an optimization-based dietary recommendation approach to improve diet scores using simulated annealing and evaluates it on Diet-Microbiome Association study data.

Kingdom-Wide CRISPR Guide Design with ALLEGRO

ALLEGRO designs universal CRISPR sgRNA libraries targeting multiple fungal species, validated experimentally across three fungi and extended to others.

Pangenome-guided sequence assembly via binary optimisation

This paper introduces a pangenome-guided sequence assembly framework that reduces contigs in complex regions using graph traversal optimisation, with potential for quantum computing implementation.

Energy Landscape Analysis with Automated Region-of-Interest Selection via Genetic Algorithms

This paper introduces ELA/GAopt, a framework using genetic algorithms to automate ROI selection in brain dynamics analysis, showing improved reproducibility and condition-specific insights in neuroimaging data.

MIDOE: Maximally-informed Design of Experiment to infer experimentally inaccessible transcription factors dynamics

The paper introduces MIDOE, a method to design experiments that optimally constrain model predictions of Smad2-Smad4 dynamics, improving predictive power and reducing experimental costs.

spOT-NMF: Optimal Transport-Based Matrix Factorization for Accurate Deconvolution of Spatial Transcriptomics

spOT-NMF improves spatial transcriptomics deconvolution by using optimal transport, outperforming existing methods in accuracy and scalability across various resolutions and species.

Chemogenetic and optogenetic strategies for spatiotemporal control of split-enzyme-based calcium recording

This paper introduces two strategies to control the timing and location of Ca2+ recording using the SCANR system: one optogenetic and one chemogenetic.

Towards model-based characterization of individual electrically stimulated nerve fibers

The paper develops a model-based approach to characterize individual electrically stimulated nerve fibers by optimizing model parameters to match single-fiber responses, achieving detailed reproduction of auditory nerve fiber responses in guinea pigs.

A Photo-regulatable Intein Based Trans-Splicing tool for Protein and Organelle Relocalization to Different Subcellular Compartments

The paper introduces PRINTS, a tool for light-controlled protein and organelle relocalization across subcellular compartments, demonstrating its versatility in targeting different cellular structures.

Spatial-scERA: A method for reconstructing spatial single-cell enhancer activity in multicellular organisms

Spatial-scERA reconstructs spatial single-cell enhancer activity in multicellular organisms using integrated transcriptomic and spatial data.

Advanced deep-tissue imaging and manipulation enabled by biliverdin reductase knockout.

This paper develops NIR imaging and optogenetic tools using Blvra-/- knockout to enhance light-controlled transcription and in vivo glucose regulation.

Gene regulatory network inference from single-cell data using optimal transport

This paper introduces GRIT, a method for inferring gene regulatory networks from single-cell data using optimal transport to model the evolution of cell distributions over time.

A Leadfield-Free Optimization Framework for Transcranially Applied Electric Currents

The paper introduces a leadfield-free optimization framework for determining optimal electrode montages in brain stimulation techniques, demonstrating its effectiveness across multiple applications.

Fair molecular feature selection unveils universally tumor lineage-informative methylation sites in colorectal cancer

This paper introduces FALAFL, a fair feature selection algorithm for single-cell methylation data, identifying universally tumor lineage-informative CpG sites in colorectal cancer.

GrAdaBeam: Combining model gradients with evolutionary search for generalizable nucleic acid design

GrAdaBeam combines gradient guidance with evolutionary search to improve nucleic acid design, outperforming existing methods across diverse genomic tasks.

Local optogenetic control of genome editing and tumorigenesis in vivo using wireless implantable optoelectronics

This study develops a wireless optogenetic system to control genome editing and tumorigenesis in mice by using light-regulated recombinase and implantable devices.

Decomposition of retinal ganglion cell electrical images for cell type and functional inference

The paper decomposes retinal ganglion cell electrical images to infer cell types and functional properties using a novel optimization-based algorithm.

Energy Optimization Induces Predictive-coding Properties in a Multicompartment Spiking Neural Network Model

The paper shows that energy optimization in a spiking neural network can produce predictive-coding properties similar to experimental observations.