Miroslava Čuperlović‐Culf

This paper surveys methods for inferring directionality in metabolic networks using stationary observations, benchmarking statistical and neural network approaches on synthetic models.

This study examines metabolic changes in brown adipose tissue during cold deacclimation, identifying rapid adaptations in N-acetylated amino acid metabolism.

The paper investigates OPA1's role in cardiac function and metabolism using in silico and in vivo approaches, finding that OPA1 overexpression improves cardiac function and maintains metabolic markers in a mouse model.

The paper develops a hybrid machine learning framework to annotate genes involved in mitochondrial glutathione metabolism and transport in cancers, identifying potential targets and demonstrating high predictive performance.

This study benchmarks network inference algorithms for deriving metabolic relationships from lipidomic and metabolomic data, revealing challenges in interpretation despite some ability to distinguish metabolic states.

ImpLiMet is a platform that helps users select the best missing data imputation method by testing eight approaches and evaluating their impact through statistical and visualization tools.

ProST provides a user-friendly tool for assessing the statistical significance of group separation in data projection analysis using PCA, t-SNE, UMAP, and LDA.

This paper introduces DAPTEV, an AI framework for designing aptamers targeting the SARS-CoV-2 spike protein, showing it can generate structurally complex aptamers with strong binding affinities.

META-BOA provides a web tool to balance metabolomics data using over-sampling algorithms and evaluates their impact through visualization and classification.

BATL is a Bayesian classifier for targeted lipidomics that annotates LC-ESI-MS/MS peaks with high accuracy using eight features, achieving over 95% correct identification in SRM datasets.

The paper investigates the role of monoamine oxidase B in delirium onset during SARS-CoV-2 infection using metabolomic profiling and computational analysis.

This study uses NMR to analyze metabolic changes in blood and cerebrospinal fluid of Alzheimer's model mice, finding that metabolite correlations are influenced by amyloid plaque formation.

YeastNet improves yeast cell segmentation in bright-field microscopy using a U-Net model, outperforming existing tools and providing a Python package for automated analysis.