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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

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Published on: November 2, 2013

Exploring the Role of Transcriptomics, Proteomics, and Machine Learning in HPV Infection and Cardiovascular Disease.

Lisa Lazzari¹, Ilaria Casati¹, Sarah Wang²

¹School of Medicine and Surgery, Università degli Studi di Milano-Bicocca, 20126 Milano, Italy.

|December 30, 2025

Summary

Human papillomavirus (HPV) infection increases cardiovascular disease (CVD) risk. Machine learning reveals new links between HPV, dilated cardiomyopathy, and cholesterol, aiding targeted therapies.

Keywords:

bioinformatics cardiovascular disease infectious disease machine learning multimorbidity proteomics transcriptomics

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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

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Published on: July 18, 2013

Area of Science:

Infectious Diseases
Cardiology
Genomics
Proteomics
Machine Learning

Background:

Human papillomavirus (HPV) is linked to various cancers, including cervical cancer.
Emerging evidence suggests HPV significantly increases the risk of cardiovascular disease (CVD), such as coronary artery disease (CAD).
The precise mechanisms of HPV's impact on CVD, particularly from a proteomics and transcriptomics standpoint, remain unclear.

Purpose of the Study:

To establish an evidence framework for applying machine learning (ML) to investigate the complex relationship between HPV and CVD.
To advance understanding of HPV's influence on CVD through proteomic and transcriptomic analyses.

Main Methods:

Utilized machine learning (ML) to analyze proteomic and transcriptomic data.
Performed bioinformatic mining of public and in-house curated databases.
Identified key proteins involved in HPV-associated cardiovascular pathogenesis.

Main Results:

Dilated cardiomyopathy (DCM) identified as a cardiovascular disease significantly modified by HPV infections, beyond known links to atherosclerosis and CAD.
Elucidated novel cholesterol-modifying mechanisms contributing to HPV's role in CVD.
Identified key proteins for further investigation using downstream ML analysis.

Conclusions:

Enhanced understanding of HPV-induced cardiovascular disease mechanisms through ML.
Development of targeted ML models for patient stratification.
Potential for optimizing patient response to novel probiotic-based therapies for HPV-related CVD.