Andrei Y. Zinovyev

The paper introduces BoNesis, a tool for inferring Boolean networks from transcriptome data to model cellular differentiation and reprogramming, demonstrating its application in hematopoiesis and adipocyte/osteoblast differentiation.

The paper presents a comprehensive genetic perturbation dataset using Cell Painting to analyze gene expression effects in human cells, revealing novel gene clusters and functional relationships.

The paper uses time-series RNA-Seq and gene regulatory network inference to study how CLL cells respond to immune signals in vitro, revealing dynamic regulatory mechanisms and patient-specific gene modules.

This paper introduces a method to link scRNA-Seq data with Boolean gene activation states, enabling both binarisation of real data and generation of synthetic data for model validation.

The paper introduces rROMA, a software package for computing gene set activity from omics data, and applies it to cystic fibrosis transcriptomic data to identify disease-related pathways and potential biases.

The paper presents a multiscale model of cancer cell invasion using PhysiBoSS, integrating spatial and intracellular processes to study migration modes and validate against experimental data.

scEvoNet predicts cell state evolution by analyzing scRNA-seq data to identify genes involved in evolutionary divergence or co-option across species or cancer datasets.

transmorph provides a flexible framework for single-cell data integration, enabling complex pipelines and demonstrating its utility in various integration tasks.

The paper shows that early pre-neural serotonin in zebrafish affects later serotonin synthesis and behavioral patterns without causing major developmental abnormalities.

The paper develops a simplified cell cycle model to analyze single-cell data and predict cell line doubling time from trajectory length.

This paper shows that reducing hubness in single-cell RNA-seq data improves clustering and trajectory inference compared to traditional dimensionality reduction methods.

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.

LNetReduce simplifies linear dynamic networks by reducing them while preserving dynamics across all time scales.

The paper provides an exact method to calculate stationary solutions and analyze parameter sensitivity in stochastic Boolean models without Monte Carlo simulations.

The paper analyzes Wikipedia's protein functional network using the REGOMAX algorithm to identify evolving hidden communities of biological functions.

The paper uses matrix factorization to analyze molecular mechanisms behind the inverse comorbidity between Alzheimer’s disease and lung cancer, identifying new candidate factors and subtype-specific mechanisms.

STREAM is an interactive pipeline for reconstructing and visualizing complex branching trajectories from single-cell transcriptomic and epigenomic data.

The paper integrates cancer signalling and metabolic networks to enable multi-omics data analysis and cross-talk studies in ovarian cancer subtypes.

The paper introduces a stabilized ICA method that improves reproducibility and interpretability of transcriptomic signals across cancer datasets, and applies it to colorectal cancer.

The paper identifies miRNA clusters driving triple negative breast cancer subtypes using a new computational pipeline and validates their role in promoting the phenotype through proliferation and EMT control.

The paper introduces the Virtual Metabolic Human database, which integrates human and gut microbiome metabolism with nutrition and disease data to support biomedical research.

PhysiBoSS integrates multi-scale agent-based modelling with Boolean networks to study cell population dynamics and treatment responses in 2D and 3D environments.

The paper uses independent component analysis to analyze melanoma transcriptomic data, distinguishing biological signals from technical biases and identifying clinically relevant patient characteristics.

The paper demonstrates the use of the CoLoMoTo Interactive Notebook to analyze a Boolean model of tumor cell invasion, predicting mutations associated with metastasis.

The paper introduces a framework for reproducible analysis of qualitative biological networks using an interactive notebook environment.

The paper discusses the application of the Atlas of Cancer Signalling Network and NaviCell tools for pre-clinical cancer studies, focusing on drug synergy and intervention design using omics data and signalling maps.

Moonlight identifies cancer driver genes and their roles by integrating functional analysis and gene regulatory networks from expression data, distinguishing tumor suppressors, oncogenes, and moonlight genes.

The paper introduces Googlomics, a method using Google matrices to infer hidden causal relations in biological networks and analyze changes during cancerogenesis.

The paper describes the construction of cancer signalling maps and introduces the NaviCell platform for their exploration and analysis.

The paper defines a set of informative gene signatures from pan-cancer data and introduces InfoSigMap to visualize their compositional and functional redundancies.