Jose M. Garcia

Full Professor

Universidad de Murcia

Spain

Research Interests

Explore related searches

Professors in Spain Professors in Medical Imaging Medical Imaging professors in Spain

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Jose M. Garcia is a Full Professor at the University of Murcia, Spain. His research areas encompass hematopoietic stem cell transplantation, cardiomyopathy detection using machine learning models, and the development of frameworks for hardware acceleration. Recently, he has published articles focusing on survival risk prediction in multiple myeloma, myocardial segmentations, and enhancing gene models to study toxicities.

Articles (19)

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2020

Collaborators (3)

Gregorio Bernabé

SPAIN

View details

FRANCISCO DE ASIS GUIL ASENSIO

Professor

Universidad de Murcia

SPAIN

View details

Fernando Jiménez

Full Professor

Universidad de Murcia

SPAIN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Jose M. Garcia

Research Interests

Explore related searches

Contact this professor

About

Articles (19)

Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma

Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection

A Real Time Cardiomyopathy Detection Tool Using Ml Ensemble Models

Expanding the deep-learning model to diagnosis LVNC: limitations and trade-offs

Adding metabolic tasks to human GEM models to improve the study of gene targets and their associated toxicities

POAS: a framework for exploiting accelerator level parallelism in heterogeneous environments

Code Detection for Hardware Acceleration Using Large Language Models

A fast method for extracting essential and synthetic lethality genes in GEM models

On the representativeness and stability of a set of EFMs

Improving a Deep Learning Model to Accurately Diagnose LVNC

Staphylococcus epidermidis RP62A’s Metabolic Network: Validation and Intervention Strategies

HDNN: a cross-platform MLIR dialect for deep neural networks

Applying Intel's oneAPI to a machine learning case study

Performance portability in a real world application: PHAST applied to Caffe

Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach

The <italic>Staphylococcus epidermidis</italic> RP62A metabolic network: Validation and intervention strategies

Deploying deep learning approaches to left ventricular non-compaction measurement

ACOTSP-MF: A memory-friendly and highly scalable ACOTSP approach

Flux Coupling and the Objective Functions’ Length in EFMs

Collaborators (3)

Gregorio Bernabé

FRANCISCO DE ASIS GUIL ASENSIO

Fernando Jiménez

Make your next move.