- Home
- Events
Filters
Sort
-
-
Filter Clear filters
-
-
Start
- -
-
-
-
Content provider
- University of Cambridge Bioinformatics Training22
- European Bioinformatics Institute (EBI)13
- Australian BioCommons5
- ELIXIR Estonia1
- ELIXIR Portugal1
- IFB French Institute of Bioinformatics1
- INB: Spanish National Bioinformatics Institute1
- University of Bradford1
- VIB Conferences1
- Show N_FILTERS more
-
-
-
Keyword
- HDRUK22
- machine learning6
- Bioimage analysis4
- Electron microscopy4
- Light microscopy4
- Machine learning models4
- Scientific computing4
- Artificial Intelligence3
- Deep learning3
- Machine Learning and Artificial Intelligence Course3
- AI2
- BioImage Archive2
- BioStudies Database2
- Bioinformatics2
- ChatGPT2
- Computational Biology2
- Data integration2
- Electron Microscopy Public Image Archive - EMPIAR2
- Imaging2
- Logic modelling2
- MOFA2
- Multi-Omics Factor Analysis2
- Network inference2
- Proteomics2
- bioinformatics2
- omics data2
- Alphafold1
- Artificial intelligence1
- Best Practices1
- Big Data1
- CAPITAL project1
- Cell-level simulations1
- ChEMBL: Bioactive data for drug discovery1
- Crop improvement1
- Data Integration1
- Data protection1
- Europe PubMed Central1
- Evolutinary genomics1
- Experimental design1
- GPU1
- Gene Expression1
- Genomics1
- HPC1
- LLM1
- Literature (literature)1
- Machine Learning1
- Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression1
- Metabolomics1
- Open source code1
- Open source tool1
- Pangenomes1
- Pangenomics1
- PerMedCoE1
- Personalised medicine1
- Plant webinar series1
- Population Genomics1
- Predictive models1
- Protein Data Bank in Europe1
- Protein biology1
- R1
- R Programming1
- R-programming1
- Reproducible Research1
- Sequence Analysis1
- Single cell1
- Structural biology1
- Supervised learning1
- Systems (Systems)1
- Systems biology1
- Systems biology, Pathway analysis, Network analysis, Microarray data analysis, Nanomaterials1
- Transcriptomics1
- algorithms1
- biomedical applications1
- biostatistics1
- clinical genomics1
- data mining and analysis1
- dynamic simulations1
- genomics1
- mutational landscapes1
- transcriptomics1
- Show N_FILTERS more
-
-
-
Scientific topic
- Knowledge representation
- Bioinformatics1099
- Genome annotation302
- Exomes299
- Genomes299
- Genomics299
- Personal genomics299
- Synthetic genomics299
- Viral genomics299
- Whole genomes299
- Biological modelling250
- Biological system modelling250
- Systems biology250
- Systems modelling250
- Biomedical research219
- Clinical medicine219
- Experimental medicine219
- General medicine219
- Internal medicine219
- Medicine219
- Data visualisation185
- Data rendering178
- Bottom-up proteomics152
- Discovery proteomics152
- MS-based targeted proteomics152
- MS-based untargeted proteomics152
- Metaproteomics152
- Peptide identification152
- Protein and peptide identification152
- Proteomics152
- Quantitative proteomics152
- Targeted proteomics152
- Top-down proteomics152
- Data management125
- Metadata management125
- Data mining113
- Pattern recognition113
- Aerobiology98
- Behavioural biology98
- Biological rhythms98
- Biological science98
- Biology98
- Chronobiology98
- Cryobiology98
- Reproductive biology98
- Comparative transcriptomics90
- Transcriptome90
- Transcriptomics90
- Exometabolomics75
- LC-MS-based metabolomics75
- MS-based metabolomics75
- MS-based targeted metabolomics75
- MS-based untargeted metabolomics75
- Mass spectrometry-based metabolomics75
- Metabolites75
- Metabolome75
- Metabolomics75
- Metabonomics75
- NMR-based metabolomics75
- Functional genomics65
- Immunology65
- Cloud computing60
- Computer science60
- HPC60
- High performance computing60
- High-performance computing60
- Computational pharmacology59
- Pharmacoinformatics59
- Pharmacology59
- Active learning50
- Ensembl learning50
- Kernel methods50
- Machine learning50
- Neural networks50
- Recommender system50
- Reinforcement learning50
- Supervised learning50
- Unsupervised learning50
- Biomathematics47
- Computational biology47
- Mathematical biology47
- Theoretical biology47
- RNA-Seq analysis41
- Pipelines40
- Software integration40
- Tool integration40
- Tool interoperability40
- Workflows40
- Data curation38
- Database curation38
- High-throughput sequencing36
- Chromosome walking35
- Clone verification35
- DNA-Seq35
- DNase-Seq35
- High throughput sequencing35
- NGS35
- NGS data analysis35
- Next gen sequencing35
- Next generation sequencing35
- Show N_FILTERS more
-
-
-
Venue
- Craik-Marshall Building22
- European Bioinformatics Institute, Hinxton3
- Computational and Data Driven Science1
- European Bioinformatics Institute1
- Fondazione Edmund Mach, Palazzo della Ricerca e della Conoscenza, Via E. Mach 1, San Michele all'Adige1
- Instituto Gulbenkian de Ciência1
- La Pedrera, 92, Passeig de Gràcia1
- Narva mnt 18, room 20341
- Provinciehuis Provincie Vlaams-Brabant Provincieplein 1 3010 Leuven Belgium1
- Provinciehuis, Provincieplein 1, Leuven1
- TBC1
- Show N_FILTERS more
-
-
-
Target audience
- Institutions and other external Institutions or individuals22
- Postdocs and Staff members from the University of Cambridge22
- Graduate students20
- This is aimed at life scientists with little or no experience in machine learning and that are looking at implementing these approaches in their research.16
- Researchers3
- This introductory course is aimed at biologists with little or no experience in machine learning.3
- <span style="color:#FF0000">After you have booked a place2
- Computational biologists2
- PhD students2
- Students and researchers from life-sciences or biomedical backgrounds2
- The course is open to Graduate students2
- if you are unable to attend any of the live sessions and would like to work in your own time2
- including for registered university students.<span style="color:#FF0000">2
- or will shortly have2
- please email the Team as Attendance will be taken on all courses. A charge is applied for non-attendance2
- post-docs2
- the need to apply the techniques presented during the course to biomedical data.2
- who have2
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £350. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £175. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £400. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £200. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- Beginner1
- Bioinformaticians1
- Computer science1
- Experimental Researchers1
- Life Science Researchers1
- Master students1
- PhD Students1
- Postdoctoral students1
- Scientists1
- Students1
- This course is aimed at advanced PhD students and post-doctoral researchers who are currently working with large-scale omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally 1-2 years) working with systems biology or related large-scale multi-omics data analyses. Applicants are expected to have a working knowledge of the Linux operating system and the ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Python turorial: https://www.w3schools.com/python/ R tutorial: https://www.datacamp.com/courses/free-introduction-to-r Regardless of your current knowledge we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
- This course is aimed at scientists working with biomage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content would also be suitable for those wanting to learn more about the BioImage Archive and gain experience with machine learning approaches for image analysis. The programme will be of particular interest to bioimage analysts with questions relating to the use of ‘big data’ and using the wealth of publically available data curated in the BioImage Archive. The course should be accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive. Applicants are encouraged to explore the resources below before starting their application. Applicants should be comfortable with basic programming tasks and have experience working with Python. Prerequisite reading: BioImage Archive: A call for public archives for biological image data ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy The BioStudies database - one stop shop for all data supporting a life sciences study EMPIAR: a public archive for raw electron microscopy image data Image Data Resource: a bioimage data integration and publication platform BioImage Model Zoo 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 workshop is aimed at researchers and bioinformaticians from across industry and academia who are looking to leverage machine learning approaches in protein function prediction. It will guide participants through the use of big data to build analytical workflows on publically-available biological data. Participants will require prior experience in the use of the command line interface and confidence in a programming language to fully benefit from the workshop. Please contact us if you have any questions about the course's suitability before you apply.1
- bioinformaticians1
- biological data analysts1
- students1
- Show N_FILTERS more
-
-
-
Language
- English1
- Show N_FILTERS more
-
- Only show online events
- Hide past events
- Show disabled events