Vitalii Stebliankin

EMoMiS: A Pipeline for Epitope-based Molecular Mimicry Search in Protein Structures with Applications to SARS-CoV-2

EMoMiS identifies molecular mimicry between SARS-CoV-2 proteins and viral/human proteins, offering insights into cross-protection and side effects.

A Comprehensive Survey of Scoring Functions for Protein Docking Models

This study surveys and evaluates state-of-the-art scoring functions for protein-protein docking, comparing classical and deep learning-based approaches.

Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

The paper introduces a visualization tool for metagenomic data using Hilbert curves to create interactive 2D microbiome maps that highlight microbial abundance patterns.

Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

Epitopedia identifies regions of molecular mimicry between pathogens and immune epitopes by analyzing structural similarity and sequence identity.

PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks

The paper introduces PIsToN, a transformer-based model for predicting protein binding interfaces, which outperforms existing methods on benchmark datasets.

Potential autoimmunity resulting from molecular mimicry between SARS-CoV-2 Spike and human proteins

The paper identifies molecular mimicry between SARS-CoV-2 Spike and human proteins that may contribute to autoimmune responses in COVID-19.

Novel Approach for Microbiome Analysis Using Bacterial Replication Rates and Causal Inference with Applications

The paper introduces a method to analyze microbiome antibiotic response using replication rates and causal inference, finding microbes reduce replication under beta-lactams and specific resistance genes enable dominance.

Causal Inference in Microbiomes Using Intervention Calculus

This paper applies causal inference techniques to microbiome data to study antibiotic resistance, introducing a novel pipeline for predicting interventional effects without controlled experiments.