Andréi S. Rodin

The paper identifies allosteric pathways that determine G protein coupling selectivity in promiscuous GPCRs using a combination of mutagenesis, simulations, and machine learning.

The paper develops an interpretable machine learning model to identify allosteric modulators of the AT1R receptor and discovers a negative allosteric modulator.

This paper introduces a causal inference framework using extrachromosomal DNA as an instrumental variable to disentangle oncogene dosage effects from downstream cellular effects in spatial multi-omics data.

The paper identifies subtype-specific conserved residues in G protein subtypes that modulate Gβγ coupling, showing conservation does not guarantee functional equivalence.

DRUMBEAT is a machine learning model that identifies time-resolved cooperative events in protein dynamics, revealing folding pathways and critical residues.

The paper uses machine learning and simulations to show that distant residue interactions in the G protein core influence GPCR selectivity and can be engineered to change signaling preferences.

This paper develops an interpretable machine learning model to identify residue communities involved in GPCR conformational transitions, with applications to dopamine receptor subtype-specific drug design.

The paper measures ligand discrimination errors and molecular noise in immune cells of ER+ breast cancer patients compared to healthy controls using a communication model.

This paper introduces a calibrated Minimum Uncertainty criterion for Bayesian network structure learning and evaluates scoring criteria's discriminative power using real biomedical data.

The paper identifies frustrated residues in GPCR:G protein interfaces and engineered degrader complexes, showing higher frustration in engineered interfaces.

The paper shows that lymphocytes and monocytes suppress IL-10R, IL-6R, and IL-2Rβγ signaling under high cytokine concentrations, mediated by cytokines that do not induce their own pSTATs.

BaNDyT uses Bayesian network modeling to analyze molecular dynamics trajectories, providing data-driven insights into protein function and interactions.

The paper develops a Bayesian network analysis pipeline to identify immune signaling changes in GEA patients before and after immunotherapy, detecting signals missed by conventional methods.

This study introduces a probabilistic Bayesian Network simulation framework to generate synthetic data that better aligns with real biological data and network models.

The paper introduces a two-stage framework using mixed mutual information and scalable Bayesian networks to analyze multimodal cancer data, identifying 11 candidate genes with 6 validated in literature.