optimal transport

The paper introduces a framework for deconvolving isotopologue profiles using moiety models and provides Python packages for optimization and model selection.

NETPHIX identifies mutated subnetworks associated with drug response by leveraging network properties and integer linear programming, revealing novel gene modules linked to MEK1/2 inhibitor sensitivity.

KOPTIC predicts enzyme kinetics and regulation using in silico data, achieving high correlation with input flux data and identifying regulatory interactions.

This paper introduces OptFill, a tool for automated gapfilling of genome-scale metabolic models that avoids thermodynamically infeasible cycles, applied to both fictional and published models.

This paper applies optimal control theory to a spatial simulation model of sudden oak death to identify effective disease management strategies that protect tanoak while conserving biodiversity.

This study introduces a network-based method combining optimal mass transport and machine learning to predict drug sensitivity in cancer cell lines by clustering genomic and chemical features.

This paper optimizes global agricultural land use to reduce ecological footprints by relocating croplands and pastures, achieving significant reductions in carbon, biodiversity, and water footprints.

This paper introduces KDiffNet, a method for estimating sparse differential Gaussian Graphical Models that integrates extra knowledge, achieving competitive performance and scalability.

The paper introduces a framework using optimal mass transport to construct a network reduction hierarchy for interactive exploration and community detection, applied to gene regulatory networks and validated on biological datasets.

The paper develops a method to generate scaled musculoskeletal neck models using anthropometric data and optimization, showing accuracy in geometry and strength.

This study introduces an optimal mass transport model to analyze contrast agent flow in head and neck cancer patients, showing comparable results to traditional methods.

This study uses optogenetic control of Wnt signaling to induce mesendoderm differentiation and gastrulation-like morphogenesis in human pluripotent stem cells.

This paper applies optimal control theory to design personalized cancer therapies, showing that individual pharmacokinetic differences significantly impact treatment outcomes.

This paper develops optimal chemotherapy strategies for AML by incorporating immune response into a stem cell model and applying optimal control theory.

The paper improves tractography by reducing false positives using anatomical priors and group-sparsity regularization in a convex optimization framework.

This paper introduces REMI, a method that integrates gene expression and metabolomic data into metabolic models to improve flux predictions and better reflect physiological states.

The paper introduces HyperPhylo, a tool for efficient data distribution in phylogenetic inference with site repeats, showing improved performance over naive methods.

This paper develops a multiobjective mathematical model to create personalized nutritional menus for children to prevent obesity by minimizing glycemic load, cholesterol, and cost.

The paper introduces an EM-LDDMM algorithm for registering 3D atlases to 2D sparse target sections, handling uncertainties through hidden variables and diffeomorphic mappings.

This paper introduces SoptSC, a method that integrates clustering, lineage inference, and communication network prediction in single-cell transcriptomics using a similarity matrix optimization framework.

The paper reduces memory requirements for long read alignment on mobile devices by optimizing parameters and merging techniques, enabling human genome alignment with 2GB RAM without accuracy loss.

The paper shows that maximizing microbial growth rate limits the number of active metabolic pathways to the number of enzyme expression constraints, explaining simple metabolic behaviors through evolutionary optimization.

The paper optimizes an RNA-seq workflow for multi-threaded environments, achieving significant speed improvements by optimizing all steps, not just alignment.

This paper unifies different optimization methods for transcranial electrical stimulation by showing they are specific cases of a general optimization framework.

This paper introduces a hierarchical and adaptive optimization algorithm to efficiently map spatiotemporal neurostimulation patterns to motor outputs in non-human primates, revealing nonlinear computations in M1.

This paper designs distributed cell classifiers using a genetic algorithm to improve accuracy and flexibility in cancer-type specific cell targeting.

This paper develops a multi-scale metabolic model of the human body with 3555 ODEs and 1826 reactions, solving 74 objectives to simulate metabolic phenotypes.

This study evaluates land prioritization strategies for conserving migratory birds by comparing annual vs. weekly optimization and inclusion of human-dominated lands.

This paper develops an optogenetic method to control mitochondrial membrane potential and shows its effects on cell fate, finding that light-induced depolarization can induce apoptosis or cytoprotection depending on duration and intensity.

PLUG improves protein design by removing biophysical model restrictions through conformational pruning, achieving effectiveness comparable to DEE for larger designs.