Navigation route

Nested applications

computational_biology_tit_general

Computational Biology

Research_Links_Areas2

computational_ancla_cima

 

cima_banner_header

Computational Biology - CIMA

WEB OF THE GROUP

Nested applications

Nested applications

computational_biology_cima_txt_intro

Molecular biology has undergone a revolution due to the ability to simultaneously study the functioning and expression of thousands of genes and proteins in the patient's body. Thanks to the use of computer technology, databases and statistical analysis we can analyse with precision and speed, large amounts of data that allow us to understand the complexity of the mechanisms that cause diseases.

CompBiologyCIMA_Title

The Computational Biology Program at the CIMA - University of Navarra currently has these lines of research:

- Analysis of transcriptomic data, both at bulk and at single cell resolution.
- Development of new file formats for storage and access to omics data.
- Machine learning methods for biomedical problems and their translation to the clinic.

tit_associated_members

ASSOCIATED MEMBERS

 

Nested applications

Nested applications

Nested applications

cima_members_mikel

Hernáez Arrazola, Mikel

Hernáez Arrazola, Mikel
coordinator from group
Web staff

Lines of research:
- Machine learning methods for biomedical problems and their translation to the clinic.

Nested applications

cima_members_ibon

Tamayo-Uria, Ibon

Tamayo-Uria, Ibon
Personal web

Lines of research:
- Bioinformatics

tit_ Guest_Members

INVITED MEMBERS

Nested applications

Nested applications

Nested applications

cima_members_juan_pablo

Romero Riojas, Juan Pablo
Personal web

Lines of research:
- Bioinformatics

Nested applications

cima_members_igor

Ruiz de los Mozos Aliaga, Igor

Ruiz de los Mozos Aliaga, Igor
Linked In

Lines of research:
- Bioinformatics

computational_ancla_digital

 

Digital_medicine_header

Computational Biology - Digital Medicine

Nested applications

Nested applications

computational_biology_digital_txt_intro

The advent of high-throughput technologies in life sciences carried revolutionary milestones in data access, management, and analysis. It also implied the development of new methodological approaches to mine these large datasets. Medicine is currently following this path with the advent of its own big data subdiscipline, namely digital medicine, where data mining through machine learning techniques constitutes its core toolkit.

computational_biology_digital_txt_intro2

The main lines of research are:

  • Accurate predictions in health care problems when confronted with uncertainty.

  • Develop fair AI-based algorithms to combine the classical models of human physiology with observations and real-time personalized data.

  • Translational research bridging theoretical approaches and practical applications in biomedical domains.

tit_Members_Attached_Members

CORE MEMBERS

 

Nested applications

Nested applications

Nested applications

Digital_Medicine_member_armaments

Rubén Armañanzas

Armañanzas, Rubén

Personal website

Lines from research:
- Explainable classification and prediction algorithms
- Trustworthy machine learning
- Digital Medicine

 

fundamentals_members_alberto

García Galindo, Alberto

García Galindo, Alberto

Lines from research:
- Fairness in Machine Learning
- Uncertainty Quantification
- Digital Medicine

Nested applications

areas_computational_marcos_lopez

López de Castro, Marcos

Lines from research:
Feature Selection;
Clinical Image Analysis;
Uncertainty Quantification;
Digital medicine.

computational_ancla_tecnun

 

TECNUN_banner_header

Computational Biology - Tecnun

WEB OF THE GROUP

Nested applications

Nested applications

computational_biology_tecnun_txt_intro

The Computational Biology group has long term experience in the development of optimization algorithms and statistical analysis. Our expertise is specifically focused in machine and deep learning with applications in human health and disease through data of high molecular resolution (genomics, transcriptomics, proteomics, metallobolomics,...) and biological databases (genomics, pharmacology, metabolism,...).

Tecnun_Txt_intro

The main lines of research are:

- Metabolic reprogramming in cancer in order to identify novel therapeutic targets and response markers.
- Integration of massive gene silencing experiments and drugs in the framework of precision oncology.
- Alternative splicing in different types of cancers: modifications, causes and effects.
- Predictive models for assessing drugs induced toxicity in human organs based on their structural features.
- Data analysis of genomic DNA.
- New methodologies to identify germline pathogenic variants in patients with cancer.
- Inference of gene regulatory network from RNA sequencing data.
- HERV (human endogenous retroviruses) characterization in human tissues and cancer cells.
- Compression techniques
- Personalized and Precision Medicine

 

tit_associated_members

ASSOCIATED MEMBERS

 

Nested applications

Nested applications

Nested applications

tecnun_idoia_occhoa

Ochoa Álvarez, Idoia
Personal web

Lines of research:
- Bioinformatics
- Compression

tecnun_francis

Planes Pedreño, Francisco
Personal web

Lines of research:
- Personalized medicine
- Analysis of biomedical data

Nested applications

tecnun_angel_rubio

Rubio Díaz-Cordovés, Ángel
Personal web

Lines of research:
- Personalized medicine
- Analysis of biomedical data

tit_guest_members

INVITED MEMBERS

Nested applications

Nested applications

Nested applications

tecnun_iñigo_apaolaza

Apaolaza Emparanza, Íñigo

Personal website

Lines of research:
- Systems Biology
- Metabolism

tecnun_fer_carazo

Carazo Melo, Fernando
Personal website

Lines of research:
- Precision Medicine
- Cancer Genomics
- Computational Biology
- Biomedical Big Data Science