- Home
- Events
Filter
Sort
-
-
Filter Clear filters
-
-
Start
- -
-
-
-
Keyword
- Advances in spatial omics5
- Data integration5
- Multiomics5
- Cytoscape3
- Spatial transcriptomics3
- networks and pathways3
- Multiomics data integration2
- Reactome pathways database2
- Spatial omics2
- Artificial Intelligence1
- Best Practices1
- BioImage Archive1
- BioSamples1
- Bioinformatics1
- Cell signalling1
- Cell-cell interactions1
- Computational Biology1
- Cross domain (cross-domain)1
- DNA & RNA (dna-rna)1
- Data Integration1
- Data curation1
- Data submission1
- Data visualisation1
- Evolutinary genomics1
- Genomics1
- HPC1
- Imaging1
- Machine Learning and Artificial Intelligence Course1
- Metadata1
- Omics Discovery Index1
- Pathway analysis1
- Plant research1
- Plant webinar series1
- Population Genomics1
- Proteins (proteins)1
- ReactomeGSA1
- Reproducible Research1
- Sequence Analysis1
- Single cell1
- Single cell RNA-seq1
- Systems biology1
- Tissue biology1
- Transcriptomics1
- algorithms1
- biomedical applications1
- cell-cell communication1
- cell-cell interaction1
- clinical genomics1
- dynamic simulations1
- machine learning1
- mutational landscapes1
- plant-microbe interactions1
- single-cells1
- Show N_FILTERS more
-
-
-
Scientific topic
- Bioinformatics709
- Genome annotation318
- Exomes309
- Genomes309
- Genomics309
- Personal genomics309
- Synthetic genomics309
- Viral genomics309
- Whole genomes309
- Biological modelling220
- Biological system modelling220
- Systems biology220
- Systems modelling220
- Biomedical research189
- Clinical medicine189
- Experimental medicine189
- General medicine189
- Internal medicine189
- Medicine189
- Data management156
- Metadata management156
- Research data management (RDM)156
- Bottom-up proteomics154
- Discovery proteomics154
- MS-based targeted proteomics154
- MS-based untargeted proteomics154
- Metaproteomics154
- Peptide identification154
- Protein and peptide identification154
- Proteomics154
- Quantitative proteomics154
- Targeted proteomics154
- Top-down proteomics154
- Cloud computing61
- Computer science61
- HPC61
- High performance computing61
- High-performance computing61
- Exometabolomics60
- LC-MS-based metabolomics60
- MS-based metabolomics60
- MS-based targeted metabolomics60
- MS-based untargeted metabolomics60
- Mass spectrometry-based metabolomics60
- Metabolites60
- Metabolome60
- Metabolomics60
- Metabonomics60
- NMR-based metabolomics60
- Immunology56
- Computational pharmacology54
- Pharmacoinformatics54
- Pharmacology54
- Comparative transcriptomics53
- Transcriptome53
- Transcriptomics53
- Data archival52
- Data archiving52
- Data curation52
- Data curation and archival52
- Data preservation52
- Database curation52
- Research data archiving52
- Biomathematics51
- Computational biology51
- Mathematical biology51
- Theoretical biology51
- FAIR data48
- Findable, accessible, interoperable, reusable data48
- Active learning46
- Ensembl learning46
- Kernel methods46
- Knowledge representation46
- Machine learning46
- Neural networks46
- Pipelines46
- Recommender system46
- Reinforcement learning46
- Software integration46
- Supervised learning46
- Tool integration46
- Tool interoperability46
- Unsupervised learning46
- Workflows46
- RNA-Seq analysis45
- Data visualisation39
- MicroRNA sequencing36
- RNA sequencing36
- RNA-Seq36
- Small RNA sequencing36
- Small RNA-Seq36
- Small-Seq36
- Transcriptome profiling36
- WTSS36
- Whole transcriptome shotgun sequencing36
- miRNA-seq36
- Metagenomics34
- Shotgun metagenomics34
- Data rendering32
- Antimicrobial stewardship30
- Show N_FILTERS more
-
-
-
Target audience
- Computational biologists1
- Computer science1
- PhD students1
- 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
-
-
-
Language
- English2
- Show N_FILTERS more
-
-
-
Instructor
- Sandra Orchard4
- Johannes Griss3
- Lee Larcombe3
- Rachel Lyne3
- Ajay Mishra2
- Asier Gonzalez2
- Denise Carvalho-Silva2
- Dezso Modos2
- Manik Garg2
- Marton Olbei2
- Pablo Porras Millan2
- Ricard Argelaguet2
- Tamas Korcsmáros2
- Tamás Korcsmáros2
- Yasset Perez Riverol2
- Alejandro Brenes Murillo1
- Alex Bateman1
- Andrew Hercules1
- Andrew Jarnuczak1
- Aurelien Dugourd1
- Britta Velten1
- Claire O’Donovan1
- Danila Bredhkin1
- Danish Memon1
- David Fazekas1
- Elena Lukyanova1
- Francesca Ciccarelli1
- Francesco Iorio1
- Gaurhari Dass1
- Girolamo Giudice1
- Gosia Trynka1
- Hanna Najgebauer1
- Helena Cornu1
- Jacques Serizay1
- Konstantinos Tsirigos1
- Livia Perfetto1
- Magnus Øverlie Arntzen1
- Maria Zimmermann1
- Masa Roller1
- Matthew Hall1
- Melissa Burke1
- Michaela Spitzer1
- Mohamed Alibi1
- Nils Eling1
- Samuel Collombet1
- Sarah Morgan1
- Sergio Contrino1
- Shila Ghazanfar1
- Swee Hoe Ong1
- Tatsuya Nobori1
- Vy Nguyen1
- Yasset Perez-Riverol1
- Show N_FILTERS more
-
- Only show online events
- Show events from all spaces
- Hide past events
- Show disabled events
- Show events with broken links