Knowledge graphs | Background | Features overview | Building graphs using specificied relationships | Minimal call | Data preparation | Building the igraph object | Visualization with Sigma.js | Providing a dictionary | Multiple nodes | Groupings | Building a fit object from a data matrix | Overview | Pair-wise similarities | Similarity threshold | Example | Building the fit object | Producing the knowledge graph | Measuring performance using known pairs | References

Introduction | OPTICS k-Xi | Models comparisons by distance-based metrics | Conclusions | Acknowledgements

New developments | Example dataset | Density-based clustering with ensemble metrics | Conclusions

Interactive Time Series Visualizations | Basic call

Summary | Background | Co-occurrence matrix | PMI | SVD | Out-of-memory SQL databases | Batching by patients | Code subsets based on dictionaries | Running on HPC servers | Installation | Parameters tuning | References

Overview | Identifying the main ICD and NLP features for phenotypes | Unsupervised MAP prediction

Building the correlation matrix plot | Adding chromosomic positions | Arranging the plots together | Adding associations values | Customizing the ggplots | Example on Crohn dataset from gap package | References | Session information

Visualization overview | Example

Introduction | Genotype data | Linkage disequilibrium | Combining association studies results | TagSNP feature selection | Conclusions | Further reading | Acknowledgements

Package overview | Character and character-shingle tokenizers | Word and word-stem tokenizers | N-gram and skip n-gram tokenizers | Sentence and paragraph tokenizers | Text chunking | Counting words, characters, sentences