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- University of Cambridge Bioinformatics Training22
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Keyword
- HDRUK
- 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
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Scientific topic
- Ensembl learning
- Bioinformatics352
- Data rendering147
- Data visualisation147
- Data mining110
- Pattern recognition110
- Aerobiology86
- Behavioural biology86
- Biological rhythms86
- Biological science86
- Biology86
- Chronobiology86
- Cryobiology86
- Reproductive biology86
- Functional genomics55
- Comparative transcriptomics50
- Transcriptome50
- Transcriptomics50
- Active learning22
- Kernel methods22
- Knowledge representation22
- Machine learning22
- Neural networks22
- Recommender system22
- Reinforcement learning22
- Supervised learning22
- Unsupervised learning22
- Python19
- Python program19
- Python script19
- py19
- Coding RNA15
- EST15
- Exomes15
- Exons15
- Fusion genes15
- Fusion transcripts15
- Gene features15
- Gene structure15
- Gene transcript features15
- Gene transcripts15
- Genome annotation15
- Genomes15
- Genomics15
- Introns15
- Personal genomics15
- PolyA signal15
- PolyA site15
- Signal peptide coding sequence15
- Synthetic genomics15
- Transit peptide coding sequence15
- Viral genomics15
- Whole genomes15
- cDNA15
- mRNA15
- mRNA features15
- Bioimaging14
- Biological imaging14
- Phylogenetics14
- ChIP-exo12
- ChIP-seq12
- ChIP-sequencing12
- Chip Seq12
- Chip sequencing12
- Chip-sequencing12
- DNA methylation9
- Epigenetics9
- Histone modification9
- Methylation profiles9
- Epigenomics8
- Exometabolomics7
- LC-MS-based metabolomics7
- MS-based metabolomics7
- MS-based targeted metabolomics7
- MS-based untargeted metabolomics7
- Mass spectrometry-based metabolomics7
- Metabolites7
- Metabolome7
- Metabolomics7
- Metabonomics7
- NMR-based metabolomics7
- Bottom-up proteomics5
- Discovery proteomics5
- MS-based targeted proteomics5
- MS-based untargeted proteomics5
- Metaproteomics5
- Peptide identification5
- Protein and peptide identification5
- Protein structure5
- Protein structure analysis5
- Protein tertiary structure5
- Proteomics5
- Quantitative proteomics5
- Targeted proteomics5
- Top-down proteomics5
- AMR4
- Antibiotic resistance (ABR)4
- Antifungal resistance4
- Antimicrobial resistance4
- Antiprotozoal resistance4
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Event type
- Workshops and courses22
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Country
- United Kingdom22
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Organizer
- University of Cambridge22
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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
- 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
- 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
- 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
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