Marco Masseroli

Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data

Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli

As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical. Challenges such as imbalances, biases, and ethical or legal constraints often limit access to high-quality data. Synthetic data generation can help overcome these limitations. Here, we …

Novel nested conformal prediction analysis to unravel complexity in patient subtyping

Linda Maldera, Marco Masseroli, Silvia Cascianelli

Patient subtyping is significantly challenged by intra-sample heterogeneity, which limits the effectiveness of traditional multi-class classification approaches enforcing mutually exclusive labels. Despite recent promising results in the transition from multi-class to multi-label classification, this process is not straightforward and proves hard to …

it (code pays fourni par la source)

mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python

Andrea Mario Vergani, Valeria Iapaolo, Emanuele Di Angelantonio, Marco Masseroli et autres

mmid (Multi-Modal Integration and Downstream analyses for healthcare analytics) is a Python package that offers multi-modal fusion and imputation, classification, time-to-event prediction and clustering functionalities under a single interface, filling the gap of sequential data integration and downstream analyses for healthcare applications …

mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python

Andrea Mario Vergani, Valeria Iapaolo, Emanuele Di Angelantonio, Marco Masseroli et autres

mmid (Multi-Modal Integration and Downstream analyses for healthcare analytics) is a Python package that offers multi-modal fusion and imputation, classification, time-to-event prediction and clustering functionalities under a single interface, filling the gap of sequential data integration and downstream analyses for healthcare applications …

it, gb (code pays fourni par la source)

BioGAN: Enhancing Transcriptomic Data Generation with Biological Knowledge

Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli

The advancement of computational genomics has significantly enhanced the use of data-driven solutions in disease prediction and precision medicine. Yet, challenges such as data scarcity, privacy constraints, and biases persist. Synthetic data generation offers a promising solution to these issues. However, existing …

it (code pays fourni par la source)

HR-SC—an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib

Luca Beltrame, Laura Mannarino, A. Sergi, Angelo Velle et autres

BACKGROUND: High-grade serous ovarian cancer (OC) patients with defects in the homologous recombination repair (HRR) pathway benefit from poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy. Clinically approved methods for identifying HRR status suffer from limitations, such as high failure rates and costs, …

it, fr, de, jp, es, at, dk, be (code pays fourni par la source)

Validation of life's simple 7 and life's essential 8 in a european cohort: the role of sleep in cardiovascular risk estimation

Sofia Mongardi, Martino F. Pengo, Marco Masseroli

Abstract Background Tools like the Life's Simple 7 (LS7), and the newly developed Life's Essential 8 (LE8) including sleep, can help estimate a person’s 10-year CVD risk. Nevertheless, recent studies in an American population showed minimal differences in CVD prediction between LS7 …

it (code pays fourni par la source)

A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification

Silvia Cascianelli, Iva Milojkovic, Marco Masseroli

OBJECTIVES: Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simplify patient molecular portraits, neglecting the potential co-occurrence of traits from multiple subtypes. …

it (code pays fourni par la source)

Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

Jacob Schreiber, Carles A. Boix, Jin wook Lee, Hongyang Li et autres

Following publication of the original article [1], the authors identified that the affiliation 1 was incorrectly used for all authors. The correct affiliations are used in this correction article and the original article [1] has been corrected.

us, jp, de, gb, it, ch, hk (code pays fourni par la source)

Enhancing Functional Interpretability in Gene Expression Analysis Through Biologically-Guided Feature Selection

Sofia Mongardi, Silvia Cascianelli, Marco Masseroli

it (code pays fourni par la source)

Inferring Breast Cancer Subtype Associations Using an Original Omics Integration Based on Non-negative Matrix Tri-Factorization

Camila Riccio‐Rengifo, Silvia Cascianelli, Gaia Ceddia, Marco Masseroli

co, it (code pays fourni par la source)

Forward and Backward Feature Selection Guided by Prior Biological Knowledge for Enhanced Interpretability

Sofia Mongardi, Silvia Cascianelli, Marco Masseroli

it (code pays fourni par la source)