Michele Ceccarelli

University of Miami

United States

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Articles (12)

Year:

2025

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2025

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2021

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2020

Collaborators (12)

Maximilian Mair

Medical University of Vienna

AUSTRIA

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concetta iside

University of Salerno

ITALY

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Emanuele Carosati

Associate Professor of Medicinal Chemistry

University of Trieste

ITALY

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Fulvio D'Angelo

Research Assistant Professor

University of Miami

UNITED STATES

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Teresa Maria Rosaria Noviello

University of Miami

UNITED STATES

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VALERIA SANTINI

ASSOCIATE PROFESSOR

University of Florence

ITALY

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Michael Weller

University Hospital of Zurich

SWITZERLAND

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Francesco Napolitano

Assistant Professor

University of Sannio

ITALY

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Michele Maio

ITALY

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Yan Guo

University of Miami

UNITED STATES

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Luigi Cerulo

Associate Professor

University of Sannio

ITALY

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Andrea Anichini

Fondazione IRCCS Istituto Nazionale dei Tumori

ITALY

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Michele Ceccarelli

Research Interests

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Articles (12)

Genome-Wide Methylome Modeling via Generative AI Incorporating Long- and Short-Range Interactions

Leveraging mRNA technology for antigen based immuno-oncology therapies

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

MoNETA: MultiOmics Network Embedding for SubType Analysis

Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network

Immunotherapy for brain metastases and primary brain tumors

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model

A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data

Pan-cancer onco-signatures reveal a novel mitochondrial subtype of luminal breast cancer with specific regulators

Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning

Structural Model for Recruitment of RIT1 to the LZTR1 E3 Ligase: Evidences from an Integrated Computational Approach

Deep learning predicts short non-coding RNA functions from only raw sequence data

Collaborators (12)

Maximilian Mair

concetta iside

Emanuele Carosati

Fulvio D'Angelo

Teresa Maria Rosaria Noviello

VALERIA SANTINI

Michael Weller

Francesco Napolitano

Michele Maio

Yan Guo

Luigi Cerulo

Andrea Anichini

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