Resources

Software Libraries & Frameworks

• SHAP (SHapley Additive exPlanations) - https://github.com/slundberg/shap
  A Python library implementing game-theoretic feature attribution methods for interpreting model outputs. Widely used for tabular, text, and biological data.

• LIME (Local Interpretable Model-agnostic Explanations) - https://github.com/marcotcr/lime
  Model-agnostic explanation technique for understanding individual predictions by approximating complex models locally with interpretable ones.

• Captum - https://captum.ai/
  An interpretability library developed by Facebook for PyTorch models, supporting integrated gradients, DeepLIFT, and other attribution methods.

• Alibi - https://github.com/SeldonIO/alibi
  An open-source Python library providing algorithms for explaining and interpreting black-box models with support for counterfactuals and anchors.

• Explainability Tools for Graph Neural Networks

  • GNNExplainer: Offers explanations for predictions made by graph neural networks by identifying important subgraphs and features.
    https://github.com/RexYing/gnn-explainer

  • GraphLIME: Local interpretable explanations for GNNs applied to biological networks.
    https://github.com/lujiarui/GraphLIME

Reproducibility & Workflow Tools

• Docker
  Containerization platform to create reproducible computational environments encapsulating code, dependencies, and system libraries.
  https://www.docker.com/

• Snakemake
  A workflow management system for building reproducible and scalable data analyses. Widely used in bioinformatics pipelines.
  https://snakemake.readthedocs.io/en/stable/

• Nextflow
  Enables portable and reproducible workflows across different computational environments, including cloud and HPC clusters.
  https://www.nextflow.io/

• Git & GitHub
  Version control and collaborative code hosting platforms to track changes and facilitate reproducible research.
  https://git-scm.com/
  https://github.com/

• Zenodo
  An open repository for sharing datasets, code, and preprints with DOI assignment, supporting open reproducible science.
  https://zenodo.org/

Tutorials & Workshops

• Interpretable Machine Learning Book (Molnar, 2020)
  Comprehensive open-access book covering theory and application of interpretability methods, with examples in R and Python.
  https://christophm.github.io/interpretable-ml-book/

• Practical tutorials on SHAP & LIME

  • Medium tutorial series by the original SHAP author: https://towardsdatascience.com/interpreting-machine-learning-models-with-shap-values-263a1b33fa7a

  • Hands-on LIME tutorial: https://towardsdatascience.com/explain-your-model-with-the-lime-python-package-21fd6e7f71e5

• Reproducible Research in Bioinformatics (Carpentries Workshop)
  Introduces tools and principles for reproducible workflows including version control and containerization.
  https://carpentries.org/lessons/

• FAIR Principles Tutorial
  Video and reading materials on implementing FAIR data principles to enhance reproducibility.
  https://www.go-fair.org/fair-principles/

Datasets & Benchmark Collections

• OpenML
  Repository of datasets with metadata and task definitions, including biological and biomedical data useful for benchmarking interpretability methods.
  https://www.openml.org/

• recount2
  Uniformly processed RNA-seq data from thousands of human samples, ideal for reproducible gene expression modeling.
  https://jhubiostatistics.shinyapps.io/recount/

• STRING Database
  Known and predicted protein-protein interactions, useful for integrating biological priors in interpretable network models.
  https://string-db.org/

• BioGRID
  Comprehensive repository of genetic and protein interaction data across multiple species.
  https://thebiogrid.org/

Community and Discussion Forums

• BioStars
  A Q&A forum focused on bioinformatics and computational biology, where interpretability and reproducibility questions are actively discussed.
  https://www.biostars.org/

• r/MachineLearning and r/Bioinformatics on Reddit
  Active communities discussing state-of-the-art ML techniques and bioinformatics workflows.
  https://www.reddit.com/r/MachineLearning/
  https://www.reddit.com/r/bioinformatics/

• ISMB (Intelligent Systems for Molecular Biology)
  Annual conference with workshops and sessions on interpretable AI and reproducible computational biology.
  https://www.iscb.org/ismb2024

• FAIRsharing.org
  A curated resource on standards, databases, and policies supporting reproducible research and FAIR data practices.
  https://fairsharing.org/