Igor Tsigelny

Professor

University of California, San Diego

United States

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Igor Tsigelny is a Professor at UC San Diego, USA. His research primarily focuses on the application of machine learning techniques in biomedical diagnostics, particularly using miRNA and piRNA biomarkers for conditions such as epilepsy, schizophrenia, autism spectrum disorder, and various forms of cancer. Recent publications illustrate his commitment to advancing diagnostic methods through innovative technological approaches.

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2025

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2020

Collaborators (3)

Vladana Vukojevic

Karolinska Institutet

SWEDEN

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Sho Oasa

Healthcare Provision, Stockholm County

SWEDEN

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Lars Terenius

Full Professor

Scripps Research Institute

UNITED STATES

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Igor Tsigelny

Research Interests

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About

Articles (20)

Application of machine learning technology and miRNA biomarkers: A novel approach for epilepsy diagnosis

Electroencephalography-based diagnosis of schizophrenia using machine learning

Development of a miRNA-based deep learning model for autism spectrum disorder diagnosis

miRNA-Based Diagnosis of Schizophrenia Using Machine Learning

Anelloviruses and Cancer

Machine-learning diagnostics of breast cancer using piRNA biomarkers

miRNA in Machine-Learning-Based Diagnostics of Oral Cancer

Implementation of Machine Learning-Based System for Early Diagnosis of Feline Mammary Carcinomas through Blood Metabolite Profiling

piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer

Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer

Small Molecule Decoys of Aggregation for Elimination of Aβ-Peptide Toxicity

miRNA in Machine-learning-based Diagnostics of Cancers

Machine‐learning‐based virtual screening to repurpose drugs for treatment of Candida albicans infection

Interactions of large T-Antigen (LT) protein of polyomaviruses with p53 unfold their cancerogenic potential

Potential SARS-CoV-2 Spike Protein-ACE2 Interface Inhibitors: Repurposing FDA-approved Drugs

Finding distinctions between oral cancer and periodontitis using saliva metabolites and machine learning

Potential SARS-CoV-2 protease Mpro inhibitors: repurposing FDA-approved drugs

Bovine leukemia virus relation to human breast cancer: Meta-analysis

Potential COVID-19 papain-like protease PLpro inhibitors: repurposing FDA-approved drugs

Deep-learning- and pharmacophore-based prediction of RAGE inhibitors

Collaborators (3)

Vladana Vukojevic

Sho Oasa

Lars Terenius

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