→ Computational Biology - CIMA
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Hernáez Arrazola, Mikel
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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.
→ Computational Biology - Digital Medicine
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Armañanzas Arnedillo, Rubén
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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 - Tecnun
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Idoia Ochoa Álvarez |
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Francisco Planes Pedreño |
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Ángel Rubio Díaz-Cordovés |
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,...).
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.
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.
Hernáez Arrazola, Mikel
PhD
coordinator from group
Web staff
Lines of research:
- Machine learning methods for biomedical problems and their translation to the clinic.
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.
The main lines of research are:
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Accurate predictions in health care problems when confronted with uncertainty.
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Develop fair AI-based algorithms to combine the classical models of human physiology with observations and real-time personalized data.
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Translational research bridging theoretical approaches and practical applications in biomedical domains.
Armañanzas Arnedillo, Rubén
PhD
Web staff
Lines from research:
- Explainable classification and prediction algorithms
- Trustworthy machine learning
- Digital Medicine
García Galindo, Alberto
Lines from research:
- Fairness in Machine Learning
- Uncertainty Quantification
- Digital Medicine
González Gomariz, Jose
PhD
Lines of research:
- Bioinformatics
- Multi-omics biomakers
- Digital Medicine
López de Castro, Marcos
Lines from research:
Feature Selection;
Clinical Image Analysis;
Uncertainty Quantification;
Digital medicine.
Oviedo Madrid, Aitor
Lines from research:
- Probalilistic graphical models
- White-box machine learning
- Cancer prognosis
- Digital medicine
Velasquez, Francisco
Lines from research:
- Mathematical Modeling and Differential Equations
- Optimization Algorithms and Machine Learning
Fernández Duval, Gonzalo
Lines from research:
- Survival Analysis
- Feature Selection
- Multi-omics biomarkers
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,...).
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
Planes Pedreño, Francisco
PhD
Web staff
Lines of research:
- Personalized medicine
- Analysis of biomedical data
Rubio Díaz-Cordovés, Ángel
PhD
Web staff
Lines of research:
- Personalized medicine
- Analysis of biomedical data
Carazo Melo, Fernando
Personal website
Lines of research:
- Precision Medicine
- Cancer Genomics
- Computational Biology
- Biomedical Big Data Science