optimal transport

Model balancing: in search of consistent metabolic states and in-vivo kinetic constants

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

Metabolite discovery through global annotation of untargeted metabolomics data

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

Trade-off between stimulation focality and the number of coils in multi-locus transcranial magnetic stimulation

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.

Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for Phylogenetics

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

Optimization algorithm for omic data subspace clustering

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

Design and assembly of DNA molecules using multi-objective optimisation

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.

Joint Alignment and Tree Inference

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.

Spatio-temporal, optogenetic control of gene expression in organoids

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.

Optimal strategies to protect a sub-population at risk due to an established epidemic

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.

Active and machine learning-based approaches to rapidly enhance microbial chemical production

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

Optimal dynamic incentive scheduling for Hawk-Dove evolutionary games

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

Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona

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

Implementation and acceleration of optimal control for systems biology

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

Studying the history of tumor evolution from single-cell sequencing data by exploring the space of binary matrices

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

Optimal balancing of clinical factors in large scale clinical RNA-Seq studies

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.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models

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

An optimality principle for locomotor central pattern generators

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.

vWCluster: A Network Based Clustering of Multi-omics Breast Cancer Data Based on Vector-Valued Optimal Transport

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.

Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx

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

Unsupervised weights selection for optimal transport based dataset integration

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.

Input-Output Slope Curve Estimation in Neural Stimulation Based on Optimal Sampling Principles

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

Resource Allocation in Annual Plants

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.

Minimizing spectral overlap in multicolor flow cytometry experiments

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

Protein allocation and enzymatic constraints explain Escherichia coli wildtype and mutant phenotypes

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

On bridging paired-end RNA-seq data

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

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

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: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding.

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

Capturing the complexity of topologically associating domains through multi-feature optimization

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: A real-coded genetic algorithm software for parameter estimation of kinetic models

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

Multiobjective optimization identifies cancer-selective combination therapies

This paper develops a multiobjective optimization method to identify cancer-selective drug combinations, validated in BRAF-V600E melanoma, showing improved selectivity through combinatorial targeting.