- Home
- Events
Filters
Sort
-
-
Filter Clear filters
-
-
Start
- -
-
-
-
Content provider
- INB: Spanish National Bioinformatics Institute1
- Show N_FILTERS more
-
-
-
Keyword
- Artificial Intelligence1
- Bioinformatics1
- Computational Biology1
- Evolutinary genomics1
- Genomics1
- HPC1
- Imaging1
- Population Genomics1
- Sequence Analysis1
- Systems biology1
- Transcriptomics1
- algorithms1
- biomedical applications1
- clinical genomics1
- dynamic simulations1
- machine learning1
- mutational landscapes1
- Show N_FILTERS more
-
-
-
Scientific topic
- Data management16
- Metadata management16
- Bioinformatics15
- Cloud computing11
- Computer science11
- Exomes11
- Genome annotation11
- Genomes11
- Genomics11
- HPC11
- High performance computing11
- High-performance computing11
- Personal genomics11
- Synthetic genomics11
- Viral genomics11
- Whole genomes11
- Research data management (RDM)5
- Biological sequences4
- Chromosome walking4
- Clone verification4
- DNA-Seq4
- DNase-Seq4
- High throughput sequencing4
- High-throughput sequencing4
- NGS4
- NGS data analysis4
- Next gen sequencing4
- Next generation sequencing4
- Panels4
- Primer walking4
- Sanger sequencing4
- Sequence analysis4
- Sequence databases4
- Sequences4
- Sequencing4
- Targeted next-generation sequencing panels4
- Bayesian methods3
- Biostatistics3
- Descriptive statistics3
- Gaussian processes3
- Inferential statistics3
- Markov processes3
- Multivariate statistics3
- Population genomics3
- Probabilistic graphical model3
- Probability3
- Statistics3
- Statistics and probability3
- Active learning2
- Data curation2
- Data privacy2
- Data provenance2
- Data security2
- Data submission, annotation, and curation2
- Database curation2
- Drug discovery2
- Ensembl learning2
- Exometabolomics2
- Kernel methods2
- Knowledge representation2
- LC-MS-based metabolomics2
- MS-based metabolomics2
- MS-based targeted metabolomics2
- MS-based untargeted metabolomics2
- Machine learning2
- Marine biology2
- Mass spectrometry-based metabolomics2
- Metabolites2
- Metabolome2
- Metabolomics2
- Metabonomics2
- MicroRNA sequencing2
- NMR-based metabolomics2
- Neural networks2
- RNA sequencing2
- RNA-Seq2
- RNA-Seq analysis2
- Recommender system2
- Reinforcement learning2
- Small RNA sequencing2
- Small RNA-Seq2
- Small-Seq2
- Supervised learning2
- Transcriptome profiling2
- Unsupervised learning2
- WTSS2
- Whole transcriptome shotgun sequencing2
- miRNA-seq2
- Aerobiology1
- Antigens1
- Behavioural biology1
- Biological modelling1
- Biological rhythms1
- Biological science1
- Biological system modelling1
- Biology1
- Biomathematics1
- Biome sequencing1
- Biomedical science1
- Show N_FILTERS more
-
-
-
Event type
- Meetings and conferences1
- Show N_FILTERS more
-
-
-
Country
- Spain1
- Show N_FILTERS more
-
-
-
Target audience
- PhD students
- Computational biologists1
- Computer science1
- Graduate students1
- Institutions and other external Institutions or individuals1
- Postdocs and Staff members from the University of Cambridge1
- Postdoctoral students1
- Researchers1
- This course is aimed at bench biologists working in the area of discovery science who want to learn more about bioinformatics tools and resources. No prior knowledge of bioinformatics is required and no experience of programming or the use of Unix / Linux is necessary.1
- This course is intended for master and PhD students, post-docs and staff scientists familiar with different omics data technologies who are interested in applying machine learning to analyse these data. No prior knowledge of Machine Learning concepts and methods is expected nor required1
- This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain a better knowledge of the biological challenges presented when working with integrated datasets. Some practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. If you are not already familiar with these then please ensure that you complete these free tutorials before you attend the course: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Basic R concept tutorials: www.r-tutor.com/r-introduction For advanced-level training in using large-scale multiomics data and machine learning to infer biological models you may wish to consider our course on Systems Biology: From large datasets to biological insight.1
- This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain a better understanding of the biological challenges when working with integrated datasets. No programming or command line experience is required to attend this course. Please note this course does not cover statistical approaches for data integration. For advanced-level training in using large-scale multiomics data and machine learning to infer biological models you may wish to consider our course on Systems Biology: From large datasets to biological insight.1
- This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain knowledge of the biological challenges when working with integrated datasets. Some practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. If you are not already familiar with these then please ensure that you complete these free tutorials before you attend the course: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Basic R concept tutorials: www.r-tutor.com/r-introduction For advanced-level training in using large-scale multiomics data and machine learning to infer biological models you may wish to consider our course on Systems Biology: From large datasets to biological insight.1
- This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists/bioinformaticians who wish to gain a better knowledge of the biological challenges presented when working with integrated datasets. Some practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. If you are not already familiar with these then please ensure that you complete these free tutorials before you attend the course: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Basic R concept tutorials: www.r-tutor.com/r-introduction1
- biocurators1
- bioinformaticians1
- software developers, bioinformaticians1
- Show N_FILTERS more
-
- Only show online events
- Hide past events
- Show disabled events