optimal transport

Inferring scaling laws of branching networks from their geometry by global optimization

This paper uses global optimization to infer scaling laws of branching networks from their geometry, revealing type-specific signatures and outperforming existing laws in vascular systems.

Optimal Conservation Prioritization for Waterfowl and Other Birds in Eastern Canada

The paper presents an optimized geospatial prioritization scheme for bird conservation in Eastern Canada using linear programming to maximize population coverage.

On Deriving Synteny Blocks by Compacting Elements

This paper introduces a formal framework for deriving synteny blocks directly from sequence data to better capture genomic rearrangements without obscuring structural variation.

Biologically inspired alignment reveals distributed and directionally asymmetric correspondence between rat and human cortices

The paper develops BrainMapping to align rat and human cortices using resting-state fMRI, revealing distributed and directionally asymmetric correspondences between species.

Identifiable inverse optimal transport for single-cell state transitions

This paper introduces UOT-IOT, an inverse optimal transport framework for single-cell state transitions, showing improved identifiability and performance over existing methods.

CardamomOT: a mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling

CardamomOT improves gene regulatory network inference and trajectory reconstruction by integrating mechanistic models with optimal transport, using exact time labels and reducing hyperparameters.

Geometric inference of cross-species transcriptome correspondence using Gromov-Wasserstein optimal transport

Species-OT uses Gromov-Wasserstein optimal transport to compare transcriptome geometries across species, identifying gene correspondences and transcriptomic discrepancies.

Optimized Multiple Circular Sequence Alignment for Cyclic Peptide Motif Discovery

The paper optimizes multiple circular sequence alignment for cyclic peptides, achieving faster performance while maintaining alignment quality.

WAH-i: Optimising Microphone Array Geometry for Customised Localisation Accuracy

This paper optimises microphone array geometry for improved localisation accuracy in bioacoustic applications, showing that geometry, not sensor count, primarily determines performance.

Predicting cellular responses to perturbation across diverse contexts with STATE

State, a set-based transformer trained on over 100 million perturbed cells, predicts perturbation responses in new cellular contexts better than mean, linear and deep learning baselines.

Gradient-based Optimization for mRNA Sequence Design

The paper introduces ID3, a framework for optimizing mRNA sequences using gradient-based methods, balancing multiple properties while preserving amino acid sequences.

Optimising fertilisation kinetics models for broadcast spawning corals in the genus Acropora

This study optimises fertilisation kinetics models for two Acropora coral species using experimental data to improve predictions of reproductive success and inform reef management.

Beyond the Default: Optimizing Molecular Networking with arteMIS

The paper introduces arteMIS, a framework for optimizing molecular network parameters in metabolomics, showing improved network quality over default settings.

Muon Reduces the Training Cost of Regulatory DNA Transformers

This paper compares Adam and Muon optimizers for training regulatory DNA Transformer models, finding Muon reduces compute costs while achieving similar performance.

UQ-PhysiCell: An extensible Python framework for uncertainty quantification and model analysis in PhysiCell

UQ-PhysiCell provides a Python framework for uncertainty quantification, calibration, and model analysis in PhysiCell simulations, enabling scalable and reproducible uncertainty-aware analysis of agent-based models.

Uncovering the spatiotemporal structure of neural avalanches through optimal transport and dynamic time warping

This paper introduces a method to analyze neural avalanches using optimal transport and dynamic time warping, revealing 12 propagation patterns and a shift toward stable patterns under psilocybin.

Breaking the Synthesis Barrier for AI-Designed DNA Libraries

This paper introduces PGLD, an AI method that designs cost-effective DNA libraries for high-throughput experiments by optimizing synthesis-aware stochastic libraries.

Scalable biophysical constraints for physiologically consistent metabolic states

The paper introduces METEOR-K, a framework that incorporates metabolite turnover rates as constraints to improve the physiological consistency of steady-state metabolic models.

ParTIpy: A Scalable Framework for Archetypal Analysis and Pareto Task Inference

ParTIpy scales archetypal analysis for biological trade-offs and integrates with single-cell workflows, enabling task inference in large datasets.

When Less Is Not More: DICEPro Mitigates the Impact of Incomplete Reference Matrices on Cellular Frequency Deconvolution.

The paper evaluates deconvolution methods' robustness to incomplete reference matrices and introduces DICEPro to improve performance by adjusting reference signatures.

SIMOFF: Discovering the metabolic objective of the cell

SIMOFF infers metabolic objectives from flux data, improving gene essentiality predictions in yeast and CHO cells.

Functional Templates in fMRI: Building Accurate and Interpretable Group-Level Decoders

This paper compares functional alignment methods and template construction strategies in fMRI to improve group-level decoding accuracy and interpretability.

DivQuant: Estimation of Species Richness and Entropy from Small Samples

DivQuant estimates species richness and entropy from small samples, achieving accurate confidence intervals and outperforming existing methods across multiple datasets.

Bi-level diversity optimisation for representative protein panel selection

This paper introduces a bi-level diversity-optimization framework for selecting representative protein panels, reducing redundancy and improving diversity compared to existing methods.

Generalist method to reconstruct metabolic networks from multi-omics data at large-scale

The paper introduces SPECTRA, a method to reconstruct metabolic networks from multi-omics data at large-scale, improving speed and reaction inclusion across cancer cell lines, microbial reconstructions, and synthetic gut microbiota.

Maximizing Nature-based Solutions using Artificial Intelligence to align global biodiversity, climate, and water targets.

This paper presents an AI framework to prioritize conservation and restoration actions that align biodiversity, climate, and water targets by optimizing habitat connectivity and co-benefits.

Topography Aware Optimal Transport for Alignment of Spatial Omics Data

TOAST improves spatial omics data alignment by integrating spatial constraints into optimal transport, enhancing accuracy and biological interpretability.

Why phylogenies compress so well: combinatorial guarantees under the Infinite Sites Model

The paper explains why phylogenetic compression works well by introducing a formal framework and showing that Neighbor Joining achieves near-optimal compression under the Infinite Sites Model.

FASTERCC: Accelerating Flux Consistency Testing and Context-Specific Reconstruction for Large-Scale Metabolic Network Models

FASTERCC improves metabolic network analysis by accelerating flux consistency testing and reconstruction, reducing runtime by 20-fold for large networks.

Branch Length Transforms using Optimal Tree Metric Matching.

This paper introduces a method to match branch lengths between trees, improving accuracy in phylogenomic analyses by addressing the challenges of heterogeneous branch length measurements.