Petr V. Nazarov

This paper uses large language models to annotate mass cytometry clusters, achieving high concordance with expert annotations and outperforming baselines.

The paper identifies impaired monocyte states and trajectories in glioblastoma patients through immune profiling, revealing systemic immune reprogramming and potential therapeutic targets.

This study integrates multi-omics and functional drug screening in patient-derived organoids to identify personalized therapeutic vulnerabilities in adult high-grade gliomas, finding distinct drug responses in IDH1 mutant and MYCN-amplified tumors.

The paper presents a comprehensive multi-omics dataset of healthy individuals, revealing sex and age influences and providing reference ranges for omics features.

ORFhunteR is a tool that accurately identifies and annotates open reading frames in human mRNA molecules using a random forest classifier trained on RefSeq and Ensembl data.

DECONbench benchmarks deconvolution methods for quantifying tumor cell-type heterogeneity by providing datasets, tools, and performance evaluation.

This paper maps the C/EBPα interactome using PRISMA and BioID, revealing PTM-dependent interactions and motifs that define its functional plasticity.

The paper identifies unisplice and multisplice splice sites in leukemia cells and shows their combinatorics follow a power-law, revealing hidden multisplice potential in unisplice sites.

This paper introduces a reference-free deconvolution protocol for DNA methylomes to identify hidden cell populations in complex samples, applied to lung cancer data from TCGA.

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 introduces a drug sensitivity prediction system using a 47-gene expression profile derived from transcriptomic analysis, validated with independent datasets and showing high concordance with observed drug responses.