Introduction | Discrete color palettes | NPG | AAAS | NEJM | Lancet | JAMA | BMJ | JCO | UCSCGB | D3 | Gephi | Observable | Primer | Atlassian | iTerm | LocusZoom | IGV | COSMIC | UChicago | Star Trek | Tron Legacy | Futurama | Rick and Morty | The Simpsons | Flat UI | Frontiers | Continuous color palettes | GSEA | Bootstrap 5 | Material Design | Tailwind CSS | Non-ggplot2 graphics | Discussion

Basic designs with gsDesignTune() | Ranking and filtering | Plot | Survival designs with gsSurvTune() | Calendar-timed analyses with gsSurvCalendarTune() | Multi-scenario exploration | Export a report

Parallel run (example) | Reproducible random search | Export a report

Preferred UX: SpendingFamily | Advanced: tune_dep() | Export a report

Introduction | Mapping table | Code example | What's next? | Acknowledgements | References

Introduction | Build survival models | Nomogram visualization | Model validation | Internal validation | External validation | Model calibration | Internal calibration | External calibration | Kaplan-Meier analysis for risk groups | Log-rank test for risk groups | Model comparison | Model comparison by validation | Model comparison by calibration | Prediction on new data | Customize color palette | Shiny app

Try it out locally | Use in production environments

Exact alignment | Deletion | Insertion with gap open | Insertion with no gap open penalty | Specify start index | Forced alignment | Acknowledgements | References

Introduction | An example predictive modeling workflow | Package overview | Commonly used descriptors | Amino acid composition descriptor | Dipeptide composition descriptor | Tripeptide composition descriptor | Autocorrelation descriptors | Normalized Moreau-Broto autocorrelation descriptors | Moran autocorrelation descriptors | Geary autocorrelation descriptors | Composition/Transition/Distribution | Composition | Transition | Distribution | Conjoint triad descriptors | Quasi-sequence-order descriptors | Sequence-order-coupling number | Pseudo-amino acid composition (PseAAC) | Amphiphilic pseudo-amino acid composition (APseAAC) | Profile-based descriptors | Proteochemometric modeling descriptors | Similarity calculation by sequence alignment | Scaling up for a large number of sequences | Similarity calculation by GO semantic similarity measures | Miscellaneous tools | Retrieve protein sequences from UniProt | Read FASTA files | Read PDB files | Sanity check for amino acid types | Remove gaps from sequences | Protein sequence partition | Auto cross covariance computation | Pre-computed 2D and 3D descriptor sets for the 20 amino acids | BLOSUM and PAM matrices for the 20 amino acids | Metadata of the 20 amino acids | ProtrWeb | Summary | References

Introduction | Walkthrough | Summary | References

Introduction | Generate data | Parameter tuning | Train the stackgbm model | Inference | Performance evaluation | xgboost | lightgbm | catboost | Tabular summary | ROC curves | Notes on categorical features | References

Introduction | Feature engineering for high-cardinality categorical features | Simulated postal code data | Recode the training set | Recode the test set | Grouping coefficients in regression models | Simulated regression data | Raw estimates | Soft-thresholding | Clustering (k = 2) | Clustering (k = 3) | Clustering while preserving data order (k = 5) | Sequential data peak calling and segmentation | Simulated time series data | Peak calling (k = 2) | Peak calling (k = 4) | Segmentation (k = 6) | References

What if my data has more categories than the number of colors offered? | Use a color scale consistently for multiple plots in a document | Customize color ordering in a palette

Introduction | Installation | How to cite Rcpi | Applications in bioinformatics | Predicting protein subcellular localization | Applications in chemoinformatics | Regression modeling in QSRR study of retention indices | In silico toxicity classification for drug discovery | Clustering of molecules based on structural similarities | Structure-based chemical similarity searching | Applications in chemogenomics | Predicting drug-target interaction by integrating chemical and genomic spaces | Compound-protein interaction (CPI) descriptors | Protein-protein interaction (PPI) descriptors | References

Retrieve protein sequence data from online databases | Retrieve drug molecular data from online databases | Calculate commonly used protein sequence derived descriptors | Generate profile-based protein representations | Generate scales-based descriptors for proteochemometrics modeling | Molecular descriptor sets of the 20 amino acids for generating scales-based descriptors | Molecular descriptors | Molecular fingerprints | Protein-protein and compound-protein interation descriptors | Similarity and similarity searching | Protein sequence data manipulation | Molecular data manipulation

Introduction | Model fitting | Cross validation | Feature importance | Outlier detection | Applicability domain evaluation | Conclusion | References

Introduction | Metadata for containerization | Required metadata | Optional metadata | Containerize the document | Render the document | Housekeeping | Install Docker

Introduction | Install Docker | Example document | Containerize the document | Render the document | Housekeeping | References

RStudio addins to the rescue | Addin: 📦 Containerize | Addin: 🎉 Render | Addin: ✂️ Prune Dangling | Addin: 🗑 Remove Image