UP v1.1 dev-preview · stable on PyPI: 1.0.3 Ecosystem Map Python packages only · the interpretable protein-feature & workflow layer between bioinformatics I/O and ML / XAI / causal / design UPSTREAM COMPLEMENTS · data in 1 · Biological data, I/O & fetch BiopythonBiotite bsbioservicesgget UniProtscikit-bio FASTA / PDB I/O · UniProt · NCBI · PDB · gget / bioservices 2 · Protein representations fair-esmtransformers bebio-embeddingsAlphaFold DB PDBBio.PDBT5ProtT5 embeddings · structure · PTM / site → pseudo-scales 4 · CORE LAYER — interpretable protein-feature layer v1.1 sequence → physicochemical scales → interpretable features → explainable ML CPP · Part × Split × Scale · CPP.run_num (numeric) · AAontology SequenceFeature · AAclust · NumericalFeature · dPULearn · AAWindowSampler TreeModel · ShapModel · CPPPlot · AAlogoPlot · AAMut · SeqMut · SeqOpt where on the sequence × how to read it × which physicochemical property vs 3 · Feature engineering input + benchmark iFiFeaturep3propy3PBPyBioMed AAanalysissingle-residue · contrastive · explained 10 · Omics inputsoptional MS · proteomics ptpyteomicspyopenmsAlphaPept single-cell · spatial scverseadAnnData DOWNSTREAM COMPLEMENTS · consume the interpretable feature matrix, explanations & objectives 5 · ML / DL modelsscikit-learnXGBoostLightGBMPyTorchcompatible · trained on matrix X6 · OptimizationOptunapmpymooDEDEAPBoTorch / Axcandidate · ΔCPP objectives7 · Protein designPRPyRosettaPMProteinMPNNRFRFdiffusionESM-IFcandidate · score / steer ΔCPP9 · XAI evaluationQuantusOXOpenXAIfaithfulness · robustness ·localization · complexitycandidate · benchmark explanations 8 · Explainability (XAI) · method taxonomy implemented in AAanalysis candidate to adopt from other packages METHOD CATEGORY AIM METHOD EXAMPLES STATUS Feature attributionlocal & global importanceSHAP · PFI · PDP · ICE · ALE · LIME · LRP · IntGrad · Grad-CAMShapModel Example-basedrepresentative & counterfactualDiMMD-CRITIC · Wachter · DiCE · CEMto adopt Rule-extractionlogical IF-THEN rulesAbANCHOR · RULE-FIT · LORE · TREPANto adopt Neural methodsexplain DL internalsLRP · IntGrad · Grad-CAM · TCAV · GNNExplainer · XGNNCaptum Non-post-hoc / complementary trust layers Surrogate modelsdistill black-boxtrain white-box on black-box predictionsto adopt Uncertainty estimationprediction confidenceMconformal prediction · MAPIEMAPIE Causal modellingcause → effectDoWhy · EconML (PyWhy)DoWhy / EconML Already in AAanalysis: ShapModel (SHAP) & TreeModel importance — the rest are candidates to adopt; CPP makes them biologically readable. CROSS-CUTTING · Model validation (protein-specific protocols) homology-aware splits · same/different-protein splits · shuffled-label controls · feature stability · per-protein AP · PU-label sanity checks | tracking: MLflow Relationship to AAanalysis Upstream complement (data in) Downstream complement (models · XAI · causal · design) Direct comparison / benchmark Optional side branch Core message: AAanalysis is the interpretable middle band — it complements the stack around it, and competes only with classical descriptor libraries, on interpretability and task-awareness rather than breadth. Maturityimplementedcandidate / futureoptional bridge
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