tutorial_catalog.yml

# Source of truth for the tutorial gallery.
#
# `conf.py`, `scripts/validate_catalog.py` and `scripts/export_landing_page.py` all read this file.
# Adding a tutorial means adding a folder plus one line here -- no Sphinx config edit required.
#
# Decision rule (see README): the folder path fixes the owning domain and the task. Everything else
# -- modality, method, fusion, dataset, difficulty -- lives in each tutorial's metadata.yml and
# surfaces as a landing-page filter.

version: 1

buckets:
  - id: protein
    label: Protein
    color: "#0f766e"
    icon: molecule
    status: active
    examples:
      - Drug-target affinity (DeepDTA)
      - Polypharmacy side effects
    summary: >-
      Molecular therapeutics. Broader than protein-only work: drug-target binding and
      polypharmacy side effects both live here.
    tutorials:
      - drug_target_affinity_prediction
      - drug_target_interaction_prediction
      - polypharmacy_side_effect_prediction

  - id: cancer
    label: Cancer
    color: "#7c3aed"
    icon: layers
    status: active
    examples:
      - Multiomics integration (MOGONET)
    summary: >-
      Primary owner for cancer applications. Tutorials here may also be tagged for biomedical or
      neurodegeneration use through their metadata.
    tutorials:
      - multiomics_cancer_classification

  - id: cardiac
    label: Cardiac
    color: "#dc2626"
    icon: pulse
    status: active
    examples:
      - Cardiac MRI diagnosis
      - Landmark uncertainty
    summary: >-
      Cardiac imaging and the uncertainty tooling that grew out of it. The quantile-binning method
      is reusable across domains, but the cardiac tutorials remain its primary owner.
    tutorials:
      - cardiac_mri_diagnosis
      - landmark_uncertainty_estimation

  - id: materials
    label: Materials
    color: "#ea580c"
    icon: lattice
    status: coming_soon
    examples:
      - Structure/property prediction
      - Crystal descriptors
    summary: >-
      Reserved. No PyKale example maps here yet -- earmarked for structure/property prediction,
      crystal and material descriptors, and materials benchmark tasks.
    tutorials: []

  # Domain-agnostic method demos. Labelled "Others" to match the folder (others/) and listed last.
  - id: others
    label: Others
    color: "#2563eb"
    icon: nodes
    status: active
    examples:
      - Domain adaptation on digits
      - Few-shot image classification
    summary: >-
      Domain-agnostic method demonstrations: image and video recognition, domain adaptation,
      few-shot learning and data loading. Start here if you are new to PyKale.
    groups:
      - id: image_classification
        label: Image classification
        tutorials:
          - cifar_cnn_transformer
          - cifar_isonet
          - digit_domain_adaptation
          - office_multisource_adaptation
    tutorials:
      - audio_visual_digit_classification
      - action_recognition
      - fewshot_image_classification
      - multisite_neuroimaging_classification
      - synthetic_domain_adaptation
      - video_data_loading

# Related projects, shown in the homepage ecosystem section. Entries without a `url` render as plain
# text (never a dead link); UKOMAIN and OMAIB have no public tutorial URL yet, so they carry none.
ecosystem:
  - name: PyKale core
    url: https://github.com/pykale/pykale
    description: The library these tutorials are built on -- multimodal and transfer learning.
  - name: PyKale data
    url: https://github.com/pykale/data
    description: Public datasets the tutorials download from.
  - name: PyKale docs
    url: https://pykale.readthedocs.io
    description: API reference for every kale module a tutorial uses.
  - name: UKOMAIN
    description: UK Open Multimodal AI Network -- the community initiative behind this work.
    planned: true
  - name: OMAIB
    description: Open Multimodal AI for Biomedicine -- biomedical applications of PyKale.
    planned: true
  - name: Domain packages
    description: Planned kale-protein, kale-cancer, kale-cardiac and kale-materials extensions.
    planned: true

# Curated routes through the tutorials. Slugs are validated against the catalog by validate_catalog.
learning_paths:
  - id: new_to_pykale
    title: New to PyKale
    summary: Start here. The gentlest path from a first run to a working domain-adaptation model.
    tutorials:
      - synthetic_domain_adaptation
      - digit_domain_adaptation
      - video_data_loading
  - id: transfer_and_domain_adaptation
    title: Transfer & domain adaptation
    summary: PyKale's core strength -- aligning models across shifting data distributions.
    tutorials:
      - digit_domain_adaptation
      - office_multisource_adaptation
      - action_recognition
      - multisite_neuroimaging_classification
  - id: multimodal_learning
    title: Multimodal learning
    summary: Combining modalities -- audio and image, graphs and sequences, multiomics.
    tutorials:
      - audio_visual_digit_classification
      - drug_target_interaction_prediction
      - multiomics_cancer_classification
  - id: biomedical_ai
    title: Biomedical AI
    summary: Applications across protein, cancer and cardiac data.
    tutorials:
      - drug_target_affinity_prediction
      - multiomics_cancer_classification
      - cardiac_mri_diagnosis
      - landmark_uncertainty_estimation