New Results
Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks
View ORCID ProfileNikolas Bernaola, View ORCID ProfileMario Michiels, View ORCID ProfilePedro Larrañaga, View ORCID ProfileConcha Bielza
doi: https://doi.org/10.1101/2020.02.05.935007
Nikolas Bernaola
1Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain
Mario Michiels
1Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain
Pedro Larrañaga
1Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain
Concha Bielza
1Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain
Posted December 16, 2020.
Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks
Nikolas Bernaola, Mario Michiels, Pedro Larrañaga, Concha Bielza
bioRxiv 2020.02.05.935007; doi: https://doi.org/10.1101/2020.02.05.935007
Subject Area
Subject Areas
- Biochemistry (11703)
- Bioengineering (8722)
- Bioinformatics (29127)
- Biophysics (14932)
- Cancer Biology (12048)
- Cell Biology (17359)
- Clinical Trials (138)
- Developmental Biology (9406)
- Ecology (14143)
- Epidemiology (2067)
- Evolutionary Biology (18268)
- Genetics (12220)
- Genomics (16766)
- Immunology (11841)
- Microbiology (28005)
- Molecular Biology (11552)
- Neuroscience (60808)
- Paleontology (450)
- Pathology (1864)
- Pharmacology and Toxicology (3231)
- Physiology (4939)
- Plant Biology (10384)
- Synthetic Biology (2877)
- Systems Biology (7333)
- Zoology (1642)