optimal transport

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

DriveWays identifies overlapping cancer driver pathways by solving an NP-hard optimization problem, outperforming existing methods on TCGA data.

This paper introduces PhASE, a system for dynamically controlling enzymatic reactions in E. coli using light-responsive compartments formed by liquid-liquid phase separation, showing enhanced reaction efficiency.

MorphOT interpolates between EM maps using optimal transport to generate morphing trajectories, avoiding blending by displacing densities.

The paper optimizes interneuron circuits for compartment-specific feedback inhibition onto pyramidal neurons using gradient-based optimization and biologically plausible models, revealing two interneuron classes resembling PV and SST.

The paper develops a generalized method to detect switch-like bistability in chemical reaction networks with conservation laws, validated on known bistable motifs.

This study shows that low-coverage single-cell RNA sequencing of more samples increases statistical power for cell-type-specific eQTL analysis compared to high-coverage sequencing of fewer samples.

The paper presents an Integer Linear Programming approach for optimal ancestral PPI network reconstruction under the DMC model, showing improved likelihood and agreement with biological evidence.

This paper develops a multicriteria optimal control framework to predict enzyme activation and metabolite concentrations in complex metabolic pathways, demonstrating its application to yeast and Bacillus subtilis.

This paper introduces an algorithm to optimize generalized gamma distributed multiplicative noise in OCT images, improving speckle removal and contrast-to-noise ratio.

This paper proposes a gait-symmetry-based human-in-the-loop optimization method for unilateral transtibial amputees, improving gait symmetry and reducing metabolic cost.

This paper introduces a data-driven method for dimensionality reduction of compositional data using amalgamation, achieving performance comparable to state-of-the-art methods while producing interpretable features.

The paper optimizes population design for genomic prediction by selecting preexisting individuals to phenotype and extending this to connected population designs for maize hybrids.

The paper introduces a method to optogenetically control regulatory RNA molecules by engineering chimeric RNAs that interact with a light-sensitive protein, enabling precise spatial and temporal regulation of gene expression.

This paper identifies cancer subnetwork markers using a multi-objective approach combining differential gene expression and network topology, achieving better classification performance than existing methods.

The paper develops a method to prioritize genomic and epigenomic experiments using imputed data to maximize information gain, and introduces a software framework called Kiwano.

This paper assesses the identifiability of tissue material parameters from uniaxial tests using synthetic data and multi-start optimization, finding that not all parameters are uniquely identifiable.

This paper presents a computational method that jointly selects epitopes and designs spacers for string-of-beads vaccines to improve immunogenicity and processing efficiency.

K-FIT accelerates kinetic parameterization using steady-state fluxomic data, achieving a thousand-fold speed-up over meta-heuristic methods for E. coli metabolic models.

The paper develops an efficient optimal scaling approach for parameter inference in dynamical models using qualitative data, improving robustness and reducing computation time.

This paper develops an optimal control theory to maximize cooperation in the prisoner's dilemma game by dynamically adjusting payoffs over time, with applications in evolutionary biology and related fields.

The paper improves the 3DMax algorithm by integrating the XNadam optimization method to better predict chromosome 3D structures from Hi-C data.

PhISCS-BnB computes the most likely perfect tumor phylogeny from single-cell sequencing data, offering optimality guarantees and significant speed improvements over existing methods.

This paper applies optimal control theory to design combination therapies for acute myeloid leukaemia by analyzing control dynamics and pay-off functions.

The paper develops an optimization framework to model nonspiking neuronal networks and applies it to uncover the mechanism of the elementary motion detector in Drosophila.

This study uses optogenetics to selectively modulate heart and respiration by targeting vagus nerve axons innervating the heart, showing distinct physiological responses compared to whole nerve stimulation.

The paper introduces SILVER, a radial sampling method for MRI that improves SNR and uniformity compared to golden ratio sampling in dynamic imaging.

This paper introduces CONETT, a method to identify conserved tumor evolution trajectories across multiple cancers, finding both previously reported and novel trajectories in renal and lung cancer datasets.

This paper presents a theoretical framework for PANDA, a gene regulatory network inference method, and shows it outperforms state-of-the-art methods in predicting transcription factor binding.

ComPotts aligns Potts models of proteins to improve pairwise sequence alignment by considering coevolutionary relationships.