optimal transport

This paper introduces a gradient descent algorithm to reconstruct protein quaternary structures from inter-chain contacts, showing it outperforms other methods and can produce high-quality models even with predicted contacts.

The paper introduces a method to estimate in-vivo metabolic kinetic constants and metabolite concentrations from omics data by solving a convex optimization problem.

The paper introduces NetID, a global network optimization method for annotating untargeted metabolomics data, identifying novel metabolites in yeast and mouse datasets.

This study investigates the trade-off between the number of coils and focality in multi-locus transcranial magnetic stimulation (mTMS) transducers, showing that wider cortical control is achievable with less focality.

TARDiS optimizes viral genome subsampling by balancing genetic diversity and temporal distribution to improve phylogenetic analysis.

The paper introduces a method for subspace clustering of omic data and shows it outperforms existing techniques in identifying ground-truth subspaces.

The paper introduces an algorithm for DNA molecule design and assembly that optimizes tradeoffs between design requirements and manufacturing constraints, showing scalability and near-optimal results.

This paper introduces a combinatorial optimization method to jointly infer multiple sequence alignments and phylogenetic trees under maximum likelihood, addressing computational limitations of existing approaches.

This study uses optogenetic control to manipulate gene expression in organoids, demonstrating its ability to generate patterned tissue structures and reveal new insights into neurodevelopmental gene regulation.

The paper uses optimal control theory to determine the best strategies for protecting a high-value sub-population during an epidemic, considering budget and uncertainty.

This work introduces ActiveOpt, a machine learning approach that reduces the number of experiments needed to optimize microbial chemical production in Escherichia coli.

This paper develops optimal dynamic incentive schedules for Hawk-Dove evolutionary games to maximize or minimize aggressive population frequency over time.

Pamona integrates heterogeneous single-cell multi-omics datasets by aligning cellular structures in a shared space while preserving dataset-specific features.

This paper reviews optimal control theory in systems biology, implements the forward-backward sweep method, and accelerates its convergence using existing techniques.

This paper reviews methods for inferring tumor evolutionary trees using binary matrix optimization and discusses a deep learning approach for topological inference.

This paper introduces BalanceIT, a method using genetic algorithms to balance clinical factors in RNA-Seq studies, improving differential gene expression and pathway analysis in autism research.

This paper introduces a method that integrates enzymatic and thermodynamic constraints into genome-scale metabolic models to improve pathway prediction accuracy.

The paper proposes an optimality principle for combining central pattern generators and reflex circuits in locomotion, showing how uncertainty-driven feedback optimizes gait stability and energy efficiency.

The paper introduces vWCluster, a network-based clustering method for multi-omics breast cancer data using vector-valued optimal transport, identifying tumor subtypes with distinct survival rates and immune microenvironment profiles.

ParProx enables scalable multivariable regression analysis of ultrahigh-dimensional omics data using group lasso with biological priors, producing interpretable models.

This paper introduces an unsupervised method to improve dataset integration by adjusting sample weights before optimal transport, showing better performance on single-cell biology data.

This paper proposes a novel input-output slope curve estimation method for neural stimulation that improves accuracy and efficiency compared to existing techniques.

The paper develops a model for resource allocation in annual plants using optimal control theory and shows that optimal growth leads to common biomass configurations regardless of initial conditions.

The paper presents an algorithm and web tool to design flow cytometry panels with minimal spectral overlap by optimizing fluorochrome combinations.

This study develops a protein allocation model to predict Escherichia coli wildtype and mutant phenotypes by integrating protein distribution with metabolic fluxes.

This paper introduces algorithms to reconstruct full RNA-seq fragments from paired-end reads, improving transcriptome assembly and expression quantification.

This paper introduces DEWAKSS, a method for denoising single-cell genomics data by optimally tuning kNN- and diffusion-based approaches to preserve biological variance and improve clustering.

HapSolo improves diploid genome assembly by removing secondary haplotigs, enhancing assembly metrics and downstream scaffolding performance across three species.

This paper develops an algorithmic framework to optimize TAD definitions and biological properties, showing that TADs vary by cell type and are better represented as subpopulations rather than a single set.

RCGAToolbox provides real-coded genetic algorithms for parameter estimation in kinetic models, offering two algorithms and a user-friendly interface.