Copertina

NeuroAI Lab: Genomic-Phenotypic Integration for AI-Driven Cancer Therapy Optimization

Fuente: Zenodo

Salvato in:

Dettagli Bibliografici
Autore principale:	Ceperkovic, Branislav
Natura:	Recurso digital
Lingua:	inglese
Pubblicazione:	Zenodo 2026
Soggetti:	Reinforcement Learning, Deep Reinforcement Learning, Precision Oncology, Cancer Therapy Optimization, Computational Oncology, Genomic Data Integration, Phenotypic Data, Multi-modal Learning, Deep Q-Network, Markov Decision Process, Personalized Medicine, Adaptive Therapy Systems Markov Decision Process (MDP) Deep Q-Network (DQN) Hindsight Experience Replay Neural Architecture Search Sequential Decision Making Computational Oncology Reinforcement Learning Deep Reinforcement Learning Precision Oncology Cancer Therapy Optimization Cancer Treatment Planning Genomic Data Analysis Personalized Medicine Cancer Biomarkers Genomic Data Integration Phenotypic Data Modeling Multi-modal Learning Clinical Data Integration AI-driven Healthcare NeuroAI Decision Support Systems Computational Medicine Adaptive Therapy Systems AI in Oncology Machine Learning in Medicine Digital Health
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

_version_	1866901660777840640
author	Ceperkovic, Branislav
author_facet	Ceperkovic, Branislav
contents	<p>This white paper introduces a conceptual reinforcement learning framework for adaptive cancer therapy optimization, integrating genomic and phenotypic data within a unified computational model. The proposed approach formulates cancer treatment as a sequential decision-making problem under uncertainty, leveraging Deep Reinforcement Learning (DRL) methods such as Deep Q-Networks and Hindsight Experience Replay to optimize therapeutic strategies over time. By incorporating multi-modal patient representations, including genomic mutation profiles and clinical phenotypic features, the framework aims to enable personalized treatment planning and dynamic adjustment of therapy protocols. Although currently theoretical, the model provides a foundation for future computational oncology systems that may improve precision medicine through AI-driven decision support and adaptive optimization of cancer treatment pathways.</p>
format	Recurso digital
id	zenodo_https___doi_org_10_5281_zenodo_19613095
institution	Zenodo
language	eng
publishDate	2026
publisher	Zenodo
record_format	zenodo
spellingShingle	NeuroAI Lab: Genomic-Phenotypic Integration for AI-Driven Cancer Therapy Optimization Ceperkovic, Branislav Reinforcement Learning, Deep Reinforcement Learning, Precision Oncology, Cancer Therapy Optimization, Computational Oncology, Genomic Data Integration, Phenotypic Data, Multi-modal Learning, Deep Q-Network, Markov Decision Process, Personalized Medicine, Adaptive Therapy Systems Markov Decision Process (MDP) Deep Q-Network (DQN) Hindsight Experience Replay Neural Architecture Search Sequential Decision Making Computational Oncology Reinforcement Learning Deep Reinforcement Learning Precision Oncology Cancer Therapy Optimization Cancer Treatment Planning Genomic Data Analysis Personalized Medicine Cancer Biomarkers Genomic Data Integration Phenotypic Data Modeling Multi-modal Learning Clinical Data Integration AI-driven Healthcare NeuroAI Decision Support Systems Computational Medicine Adaptive Therapy Systems AI in Oncology Machine Learning in Medicine Digital Health <p>This white paper introduces a conceptual reinforcement learning framework for adaptive cancer therapy optimization, integrating genomic and phenotypic data within a unified computational model. The proposed approach formulates cancer treatment as a sequential decision-making problem under uncertainty, leveraging Deep Reinforcement Learning (DRL) methods such as Deep Q-Networks and Hindsight Experience Replay to optimize therapeutic strategies over time. By incorporating multi-modal patient representations, including genomic mutation profiles and clinical phenotypic features, the framework aims to enable personalized treatment planning and dynamic adjustment of therapy protocols. Although currently theoretical, the model provides a foundation for future computational oncology systems that may improve precision medicine through AI-driven decision support and adaptive optimization of cancer treatment pathways.</p>
title	NeuroAI Lab: Genomic-Phenotypic Integration for AI-Driven Cancer Therapy Optimization
topic	Reinforcement Learning, Deep Reinforcement Learning, Precision Oncology, Cancer Therapy Optimization, Computational Oncology, Genomic Data Integration, Phenotypic Data, Multi-modal Learning, Deep Q-Network, Markov Decision Process, Personalized Medicine, Adaptive Therapy Systems Markov Decision Process (MDP) Deep Q-Network (DQN) Hindsight Experience Replay Neural Architecture Search Sequential Decision Making Computational Oncology Reinforcement Learning Deep Reinforcement Learning Precision Oncology Cancer Therapy Optimization Cancer Treatment Planning Genomic Data Analysis Personalized Medicine Cancer Biomarkers Genomic Data Integration Phenotypic Data Modeling Multi-modal Learning Clinical Data Integration AI-driven Healthcare NeuroAI Decision Support Systems Computational Medicine Adaptive Therapy Systems AI in Oncology Machine Learning in Medicine Digital Health
url	https://doi.org/10.5281/zenodo.19613095