diff --git a/README.md b/README.md index 016a8f0..64f777d 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,10 @@ # TargetIntel-IO -[![Tests](https://github.com/rsolerortuno/TargetIntel-IO/actions/workflows/tests.yml/badge.svg)](https://github.com/rsolerortuno/TargetIntel-IO/actions/workflows/tests.yml) -[![Latest release](https://img.shields.io/github/v/release/rsolerortuno/TargetIntel-IO)](https://github.com/rsolerortuno/TargetIntel-IO/releases/latest) -[![Python](https://img.shields.io/badge/Python-3.11-3776AB.svg)](https://www.python.org/) -[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) +[![Tests](https://github.com/rsolerortuno/TargetIntel-IO/actions/workflows/tests.yml/badge.svg)](https://github.com/rsolerortuno/TargetIntel-IO/actions/workflows/tests.yml) [![Latest release](https://img.shields.io/github/v/release/rsolerortuno/TargetIntel-IO)](https://github.com/rsolerortuno/TargetIntel-IO/releases/latest) [![Python](https://img.shields.io/badge/Python-3.11-3776AB.svg)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) **Explainable, therapeutic-intent-aware target intelligence for anti-PD-1-resistant melanoma.** -TargetIntel-IO is a reproducible scientific software project for classifying, prioritizing, and explaining candidate therapeutic targets and biomarkers. It combines a deterministic translational-biology baseline with auditable evidence layers for literature, target feasibility, functional genomics, and future single-cell, spatial, and clinical-response data. +TargetIntel-IO is a reproducible scientific software project for classifying, prioritizing, and explaining candidate therapeutic targets and biomarkers. It combines a deterministic translational-biology baseline with an emerging, auditable evidence layer for literature, functional genomics, single-cell, spatial, and clinical-response data. > **Not simply “What is the best target?” but “Best candidate for which therapeutic intent, supported by which evidence, and with which limitations?”** @@ -15,348 +12,349 @@ TargetIntel-IO is a reproducible scientific software project for classifying, pr | Layer | Status | Purpose | |---|---|---| -| **v0.1.3 deterministic translational baseline** | Complete | Stable role classification, modality reasoning, therapeutic-intent rankings, benchmark and sensitivity analysis | -| **v0.2.0 Common Evidence Layer** | Complete | Immutable evidence contracts, provenance, validation, storage and reporting | -| **v0.3.0 Grounded Literature Copilot and provider-agnostic LLM integration** | Complete architecture and offline demo | Grounded evidence synthesis without changing deterministic rankings | -| **v0.4.0 Target feasibility and expanded Open Targets integration** | Complete | Target feasibility profiles and reproducible Open Targets ingestion | -| **v0.5.0 DepMap/CRISPR functional dependency** | Complete research preview | Reproducible dependency profiling, benchmarking and bounded post-ranking integration | -| **v0.6.0 Single-cell and spatial context** | Roadmap | Cell-state, compartment and spatial-context evidence | -| **v0.7.0 Clinical-response research model** | Roadmap | Research-only response-cohort analysis | -| **v0.8.0 De novo target discovery and knowledge graph** | Roadmap | Evidence-connected discovery and graph reasoning | -| **v1.0.0 Multitumor target-intelligence platform** | Roadmap | Generalized, disease-aware target intelligence | +| **v0.1.3 deterministic baseline** | Available | Transparent target classification, therapeutic-intent ranking, benchmark evaluation, hypothesis cards, reports, and sensitivity analysis | +| **v0.2.0 Common Evidence Layer** | Complete | Typed contracts, validation, immutable provenance, storage, retrieval, and post-ranking report decoration | +| **v0.3.0 grounded-evidence infrastructure** | Complete | Provider-neutral execution, audited extraction, mandatory review, reviewed snapshots, grounded synthesis, and safe Markdown export | +| **v0.4.0 target feasibility** | Complete | Offline deterministic feasibility retrieval, normalized profiles, coverage reporting, modality composition, and post-ranking presentation | -**Important boundary:** v0.2.0 is infrastructure and report decoration. It is optional, read-only and post-ranking; it cannot silently alter deterministic scores, roles, benchmark outputs or sensitivity results. +v0.2.0 is infrastructure and report decoration, not clinical validation or a production LLM extractor. v0.3.0 remains research infrastructure and does not alter deterministic scores, rankings, or role classification. -A production LLM extractor is not enabled. LLM-assisted workflows remain grounded, provider-agnostic and separated from production ranking decisions. +v0.4.0 adds a separate, descriptive feasibility layer after deterministic prioritization. It can retrieve an explicit directed target universe independently of association rank and report its coverage, while retaining unresolved, no-record, and failed outcomes. Source-linked profiles retain clinical precedence, modality-specific tractability, doability, safety-data state, missingness, and contradictions. Feasibility does not change scores, roles, ranks, ordering, or selection; missing safety data does not mean safety. +Run the fully offline deterministic demonstration: -See the [TargetIntel-IO 2.0 roadmap](docs/ROADMAP_2_0.md), the [v0.2.0 evidence-layer specification](docs/specs/v0.2.0_evidence_layer.md), and the [v0.5.0 DepMap release notes](docs/releases/v0.5.0.md). +```bash +python examples/feasibility/run_v040_mock_demo.py --output-dir /tmp/targetintel-v040-demo +``` -## Why this project exists +See the [feasibility example](examples/feasibility/README.md) and [v0.4.0 release notes](docs/releases/v0.4.0.md). -Public target-discovery resources can retrieve hundreds of disease-associated genes, but association does not automatically imply therapeutic value. +### v0.3.0 evidence-to-synthesis boundary -A candidate may be: +The original deterministic pipeline still performs target classification and therapeutic-intent scoring. The separate v0.3.0 path moves source-linked observations through provider-neutral extraction, audit, mandatory human review, explicit persistence, immutable reviewed snapshots, and cited target-level synthesis. It creates no score, ranking, role, or treatment recommendation. Human approval permits controlled software promotion only; it is not scientific or clinical validation. Obsidian is an optional rendered destination, never a scientific source of truth. -- a direct therapeutic target; -- an immunotherapy-combination opportunity; -- a resistance biomarker; -- a patient-stratification marker; -- a mechanistic resistance gene; -- a tumor-intrinsic driver; -- an immune-context marker; -- biologically relevant but poorly tractable; -- or a poor direct target. +Run the fully offline synthetic demonstration: -TargetIntel-IO separates these interpretations and ranks candidates according to the intended therapeutic use. +```bash +python examples/llm/run_v030_mock_demo.py --output-dir /tmp/targetintel-v030-demo +``` -## Core design +See the [demo guide](examples/llm/README.md) and [v0.3.0 release notes](docs/releases/v0.3.0.md). -The framework separates two decisions: +### Version roadmap and current release -1. **Stable translational role** - What kind of biological or therapeutic entity is the candidate? +v0.5.0 DepMap/CRISPR functional dependency is complete as a reproducible research-preview workflow: 56 benchmark targets, 331 discovery targets, 18,531 background genes, 100% benchmark and holdout coverage, a preserved 300-target baseline, disabled automatic activation, and zero differing scientific artifacts across two independent runs. -2. **Therapeutic-intent-aware priority** - How useful is the candidate for a particular intervention or biomarker question? +Roadmap: v0.2.0 Common Evidence Layer; v0.3.0 Grounded Literature Copilot and provider-agnostic LLM integration; v0.4.0 Target feasibility and expanded Open Targets integration; v0.6.0 Single-cell and spatial context; v0.7.0 Clinical-response research model; v0.8.0 De novo target discovery and knowledge graph; v1.0.0 Multitumor target-intelligence platform. See the [v0.5.0 release notes](docs/releases/v0.5.0.md) and [repository-safe evidence](docs/releases/evidence/v0.5.0/). -The first implemented ranking profiles are: +## Why this project exists -- **Antibody / immuno-oncology combination** -- **Resistance biomarker** -- **Tumor-intrinsic / small-molecule intervention** +A biologically relevant gene is not automatically a good drug target. The same gene may instead be: + +- a direct therapeutic target; +- an anti-PD-1 combination target; +- a resistance biomarker; +- a mechanistic resistance marker; +- a tumor-intrinsic driver; +- an immune-context signal; +- or a poor direct therapeutic candidate. + +TargetIntel-IO makes these distinctions explicit and preserves the reasoning behind each classification and ranking rather than returning one opaque score. ## Architecture ```mermaid flowchart TD OT[Open Targets] --> FT[Deterministic feature table] - ONT[Resistance ontology] --> FT - MOD[Modality and tractability rules] --> FT - SAFE[Safety and evidence-density signals] --> FT - - FT --> ROLE[Stable role classification] - ROLE --> R1[Antibody / IO ranking] - ROLE --> R2[Biomarker ranking] - ROLE --> R3[Small-molecule ranking] - - R1 --> REP[Cards, reports and figures] + CFG[Curated resistance and modality rules] --> FT + FT --> RC[Stable biological-role classifier] + RC --> R1[Antibody / IO ranking] + RC --> R2[Resistance-biomarker ranking] + RC --> R3[Small-molecule ranking] + R1 --> REP[Cards, HTML reports, figures] R2 --> REP R3 --> REP LIT[Scientific literature] -->|v0.2 complete| EI[Normalized EvidenceItems] - EI --> VAL[Deterministic validation] - VAL --> DB[(Immutable evidence store)] - DB --> EVR[Evidence-aware reports] - - DEP[DepMap / CRISPR] -->|v0.5 complete| FD[Dependency profiles and bounded overlay] - FD --> REP - + DEP[DepMap / CRISPR] -->|v0.5 complete| FD[Dependency profiles and bounded overlay] --> REP SC[Single-cell / spatial] -. roadmap .-> EI CLIN[Clinical cohorts] -. roadmap .-> EI + EI --> VAL[Deterministic validation] + VAL --> DB[(Immutable DuckDB store)] + DB --> EVR[Evidence-aware reports] + DB -. future .-> LLM[Grounded LLM reasoner and critic] + LLM --> EVR + REP --> EVR ``` -The deterministic baseline remains authoritative by default. Evidence and functional-dependency layers add auditable context rather than silently replacing the production ranking. +### Evidence before interpretation + +The LLM is not intended to be the source of truth. Future model-generated interpretations must be derived only from stored, source-linked evidence. TargetIntel-IO separates: + +1. retrieved or computed observations; +2. system-generated interpretations; +3. target-level recommendations. + +Recommendations must remain traceable to the exact observations, quotations, datasets, cohorts, experiments, and transformations that support them. + +## What the deterministic workflow produces + +For every candidate, the workflow generates: + +- a stable biological and translational role; +- a therapeutic direction; +- matched anti-PD-1 resistance programs; +- modality-fit assessments; +- evidence supporting and arguing against prioritization; +- confidence and uncertainty annotations; +- separate rankings for three therapeutic intents; +- structured Markdown hypothesis cards; +- browsable HTML reports; +- summary figures and rank-shift analyses. + +| Mode | Prioritizes | +|---|---| +| **Antibody / IO combination** | Surface-accessible checkpoints, myeloid targets, suppressive immune axes, and combination rationale | +| **Resistance biomarker** | Antigen-presentation loss, IFNγ resistance, immune exclusion, and patient-stratification potential | +| **Small molecule** | Tumor-intrinsic drivers, kinases, oncogenic pathways, and small-molecule tractability | + +## Evidence-layer example + +```python +EvidenceItem( + evidence_id="ev_b2m_example", + target_symbol="B2M", + disease_name="melanoma", + disease_id="MONDO:0005105", + treatment_name="anti-PD-1", + evidence_type="clinical_cohort", + evidence_direction="supports_biomarker", + observation="Source-grounded observation stored separately from interpretation.", + interpretation=None, + source="Europe PMC", + source_id="PMID:...", + quoted_span="Exact supporting source text.", + patient_cohort_id="cohort_identifier", + species="human", + model_system="patient_tumor_biopsy", + extraction_method="llm", + validation_status="citation_verified", +) +``` -## v0.5.0 real-data release closure +The evidence layer rejects records that claim verification without the required quotation, support, provenance, identifiers, and validation history. Sharing `source` and `source_id` does not automatically make two observations revisions of one another; revision links are explicit and caller-driven. -The complete v0.5.0 workflow was executed locally using `DepMap_Public_26Q1`. +## Human-supervised multi-LLM development -### Frozen scope +Recent v0.2.0 work uses a human-supervised multi-agent development workflow: -| Item | Result | -|---|---:| -| Reviewed cutaneous melanoma models | 56 | -| Acral melanoma sensitivity models | 4 | -| Curated benchmark targets | 56 | -| Discovery universe after benchmark union | 331 | -| DepMap background genes | 18,531 | -| Benchmark coverage | 100% | -| Holdout coverage | 100% | -| Differing scientific artifacts across independent runs | 0 | - -All required stages completed: - -1. full-release ingestion; -2. requested-target ingestion; -3. universe freezing; -4. dependency profiling; -5. benchmark evaluation; -6. bounded integration. - -The final state was: - -```text -ready_research_preview_human_review +```mermaid +flowchart LR + H[Human scientific objective] --> S[Gemini-assisted specification] + S --> I[Codex-assisted implementation] + I --> T[Automated tests and regression gates] + T --> R[Independent Claude review] + R --> A[Adversarial audit] + A --> H2[Human approval and merge] ``` -The original 300-target antibody/IO baseline remained preserved. Automatic candidate activation remained disabled and any candidate decision requires separate human review. +Shared agent instructions require the system to: -Two independent persistent runs and their internal replicas were reproducible. The shared scientific closure identity was: +- never invent biological evidence, numerical values, references, or API data; +- never present association as proof of causality; +- preserve observation separately from LLM interpretation; +- prevent future LLM components from silently changing baseline rankings; +- protect secrets and identifiable patient-level information; +- report unresolved failures explicitly; +- require human approval before publication or merge. -```text -v050closure_e57fa135ff266078d2170bf2a34df094f7888e7ce6002783c75f6a583690a3a4 -``` +See [`AGENTS.md`](AGENTS.md) and [`CLAUDE.md`](CLAUDE.md). -Repository-safe evidence is available in [`docs/releases/evidence/v0.5.0/`](docs/releases/evidence/v0.5.0/). +> Agentic AI currently helps engineer and review the platform. A production +> scientific LLM agent remains a roadmap feature. -## Biological context +## Quick start -The first disease context is anti-PD-1-resistant melanoma. +### Conda -The resistance ontology includes: +```bash +git clone https://github.com/rsolerortuno/TargetIntel-IO.git +cd TargetIntel-IO +conda env create -f environment.yml +conda activate targetintel +``` -- checkpoint redundancy and T-cell exhaustion; -- antigen-presentation loss; -- IFNγ-pathway resistance; -- suppressive myeloid states; -- Treg-mediated suppression; -- TGFβ/CAF-driven exclusion; -- immune-cold states; -- metabolic immune suppression; -- melanoma plasticity and dedifferentiation; -- tumor-intrinsic driver biology. +### Pip -## Stable role classification +```bash +git clone https://github.com/rsolerortuno/TargetIntel-IO.git +cd TargetIntel-IO +python -m venv .venv +source .venv/bin/activate +python -m pip install --upgrade pip +python -m pip install -e ".[dev]" +``` -Candidate roles include: +## Run the deterministic workflow -- direct therapeutic target; -- anti-PD-1 combination target; -- resistance biomarker; -- patient-stratification biomarker; -- mechanistic resistance marker; -- tumor-intrinsic driver; -- immune-context marker; -- poor direct therapeutic target; -- unclear or low-confidence candidate. +```bash +targetintel run +targetintel run --validate +targetintel run --refresh +targetintel run --help +``` -The classifier explicitly distinguishes: +## Main outputs ```text -therapeutic target ≠ biomarker ≠ resistance mechanism ≠ poor direct target +data/processed/ +└── targetintel_feature_table_v0_1.csv + +results/ +├── ranked_targets.csv +├── target_cards/ +├── html_reports/ +│ └── index.html +├── figures/ +├── benchmark/ +└── sensitivity/ ``` -## Modality-aware reasoning +Versioned examples: -The framework evaluates whether a candidate is compatible with: +- [`examples/html_reports/`](examples/html_reports/) +- [`examples/figures/`](examples/figures/) +- [`examples/benchmark/`](examples/benchmark/README.md) +- [`examples/sensitivity/`](examples/sensitivity/README.md) -- antibody or bispecific targeting; -- small-molecule intervention; -- biomarker use; -- patient stratification; -- immunotherapy combination; -- pathway restoration or reprogramming; -- or no credible direct therapeutic modality. +## How the deterministic baseline works -Relevant signals include tractability, localization, known drugs, clinical phase, normal-tissue expression, safety concerns and whether the evidence supports causality or only association. +1. **Public evidence retrieval:** melanoma-associated targets are retrieved from +the Open Targets GraphQL API and cached locally. +2. **Feature construction:** targets are annotated with disease association, +resistance-axis membership, modality fit, tractability, known drugs, safety, contradictions, completeness, and confidence. +3. **Stable role classification:** each candidate receives one role independent +of ranking mode. +4. **Therapeutic-intent scoring:** candidates are scored separately for +antibody/IO, biomarker, and small-molecule use. +5. **Human-readable outputs:** rankings are converted into cards, reports, +figures, benchmark summaries, and machine-readable validation outputs. -## Evidence-for and evidence-against - -Each target hypothesis records both supporting and opposing evidence. - -Supporting evidence may include: - -- melanoma association; -- resistance-axis relevance; -- relevant tumor or immune-cell expression; -- surface accessibility; -- known tractability; -- clinical development; -- mechanistic combination rationale; -- intent-specific fit. - -Opposing evidence may include: +```text +therapeutic target ≠ biomarker ≠ resistance mechanism ≠ contextual marker +``` -- intracellular or nuclear localization; -- broad normal-tissue expression; -- essentiality or toxicity risk; -- weak resistance-specific evidence; -- marker-versus-cause ambiguity; -- contradictory findings; -- crowded target space; -- missing or low-confidence evidence. +## Internal benchmark snapshot -## Benchmark interpretation +TargetIntel-IO includes a curated 56-target benchmark for internal rule-based sanity validation. -The curated benchmark contains 56 targets spanning checkpoint biology, antigen presentation, IFNγ resistance, myeloid suppression, metabolic suppression, stromal exclusion, melanoma plasticity and tumor-intrinsic drivers. +| Metric | Result | +|---|---:| +| Benchmark targets evaluated | 56 / 56 | +| TargetIntel evaluation coverage | 100% | +| Open Targets top-300 retrieval coverage | 44.6% | +| Stable-role accuracy | 100.0% | +| Strict primary-intent accuracy | 91.1% | +| Acceptable-intent accuracy | 100.0% | +| Cross-intent specificity | 90.6% | +| Control not-prioritized rate | 100.0% | +| Mean top-10 recall | 58.1% | +| Mean top-20 recall | 79.5% | -Only **25/56 (44.6%)** benchmark targets appeared among the top 300 melanoma associations retrieved from Open Targets. TargetIntel evaluation coverage therefore does not mean that Open Targets independently recovered every benchmark target. +Only **25/56 (44.6%)** benchmark targets appeared among the top 300 melanoma associations retrieved from Open Targets. TargetIntel evaluation coverage therefore does not mean that Open Targets independently recovered every target. -The deterministic benchmark evaluates implementation consistency with curated biological expectations. It does not constitute independent clinical validation, prospective predictive performance or proof of therapeutic efficacy. +The benchmark produced **100.0% stable-role accuracy**, **91.1% strict primary-intent accuracy**, and **100.0%** acceptable-intent accuracy. Expected roles and acceptable alternatives were internally curated rather than derived from an independent benchmark. These results measure implementation consistency, not independent biological accuracy. -Complete benchmark outputs are available in [`examples/benchmark/`](examples/benchmark/README.md). +No external patient-level responder/non-responder cohort was used for this internal benchmark. The complete results are available in the [versioned benchmark snapshot](examples/benchmark/README.md). ## Weight sensitivity -The local sensitivity workflow evaluates 42 scenarios by changing one scoring weight by `-20%` or `+20%` before renormalization. - -The analysis measures ranking stability around the configured baseline. It does not prove that the selected weights are biologically optimal or that rankings are independent of modelling choices. - -Complete sensitivity outputs are available in [`examples/sensitivity/`](examples/sensitivity/README.md). - -## Evidence layer +The local analysis evaluates **42 scenarios**, changing one scoring weight by `-20%` or `+20%` before renormalization. -The v0.2.0 Common Evidence Layer provides: +Worst-case top-5 retention was: **antibody/IO 100%, biomarker 100%, small-molecule 80%**. -- immutable typed evidence records; -- canonical serialization and hashing; -- explicit provenance; -- validation and rejection reasons; -- DuckDB and Parquet storage; -- deterministic read-only reporting; -- source-aware limitations; -- safeguards against score or ranking mutation. +Worst-case top-10 retention was: **antibody/IO 90%, biomarker 100%, small-molecule 90%**. -Evidence reporting is optional. The deterministic pipeline can run without it. +Worst-case top-20 retention was: **antibody/IO 100%, biomarker 95%, small-molecule 100%**. -## Installation +The minimum observed Spearman correlation was **0.8762**. The maximum absolute change in strict primary-intent accuracy was **5.36 percentage points**; the maximum acceptable-intent change was **3.57 percentage points**; and the maximum cross-intent-specificity change was **5.66 percentage points**. -Create the Conda environment: +![Worst-case ranking stability](examples/sensitivity/sensitivity_overview.png) -```bash -conda env create -f environment.yml -conda activate targetintel -``` - -For the exact Python 3.11 environment used by continuous integration: - -```bash -python -m pip install --require-hashes --requirement requirements-lock.txt - -python -m pip install --no-deps --no-build-isolation --editable . -``` +This is a local stability analysis. It does not prove that the selected weights are biologically optimal or that the rankings are independent of weight choice. -## Tests +## Reproducibility and software quality -Run the complete test suite before opening or merging a pull request: +The project includes: -```bash -python -m pytest -q -``` +- a reusable Python package and command-line interface; +- compatible dependency ranges in `pyproject.toml`; +- a Conda environment definition; +- an exact Python 3.11 lockfile with package hashes; +- deterministic ranking and tie-breaking; +- versioned benchmark, sensitivity, and DepMap release-closure evidence; +- GitHub Actions continuous integration; +- offline unit and regression tests; +- immutable evidence storage and Parquet verification; +- scientific and AI-agent safety instructions. -Run documentation consistency checks directly: +Install the exact locked environment used by CI: ```bash -python -m pytest tests/test_release_documentation.py -q +python -m pip install \ + --require-hashes \ + --requirement requirements-lock.txt + +python -m pip install \ + --no-deps \ + --no-build-isolation \ + --editable . ``` -Check formatting before committing: +Run tests: ```bash -git diff --check +python -m pytest tests -q ``` -GitHub Actions runs the test suite on pushes and pull requests to `main`. Pull requests should only be merged after all required checks pass. - ## Repository map ```text -configs/ Disease context, resistance axes, benchmark and scoring -targetintel/ Reusable Python package and command-line workflows -targetintel/evidence/ Typed evidence contracts, validation and storage +configs/ Disease context, resistance axes, benchmark, scoring +targetintel/ Reusable Python package and command-line workflow +targetintel/evidence/ Typed evidence contracts, validation, immutable storage scripts/ Pipeline and snapshot-management commands -tests/ Unit, integration, regression and documentation tests -examples/ Versioned reports, figures, benchmark and sensitivity -docs/ Architecture, roadmap, specifications and release evidence +tests/ Unit, integration, and regression tests +examples/ Versioned reports, figures, benchmark, sensitivity +docs/ Architecture, roadmap, specifications, release evidence data/ Local cached and processed data; not versioned results/ Generated local outputs; not versioned ``` -## Reproducibility and governance - -The project includes: - -- deterministic ranking and tie-breaking; -- immutable release and configuration identities; -- versioned benchmark and sensitivity snapshots; -- repository-safe release evidence; -- checksum inventories; -- internal replica comparison; -- independent run-to-run comparison; -- continuous integration; -- offline unit and regression tests. - -The workflow uses public data and curated public biological knowledge, principally Open Targets Platform and DepMap Public releases. - -No confidential, proprietary, company-internal or identifiable patient data is included. Source matrices, generated databases, local caches and complete run directories remain outside version control. Only portable, sanitized evidence, summaries and checksums are committed. - ## Scope and limitations -TargetIntel-IO is a hypothesis-generation and target-triage framework. It does not provide: +TargetIntel-IO is a hypothesis-generation and target-triage framework. It does not provide clinical recommendations, validated therapeutic targets, qualified biomarkers, causal biological proof, a diagnostic system, patient-level treatment predictions, or medical advice. -- clinical recommendations; -- validated therapeutic targets; -- qualified biomarkers; -- causal biological proof; -- patient-level treatment predictions; -- diagnostic decisions; -- medical advice. +The current deterministic implementation focuses on anti-PD-1-resistant melanoma. v0.2.0 evidence reporting remains optional and read-only. v0.5.0 adds reproducible DepMap/CRISPR dependency profiling as an explanatory post-ranking layer: the production baseline remains preserved and candidate activation requires separate human review. Production LLM extraction, single-cell/spatial integration, patient-response modelling, and knowledge-graph inference remain future work. -DepMap cell-line dependency is contextual functional-genomics evidence, not direct evidence of anti-PD-1 response or clinical efficacy. +All generated hypotheses require independent experimental, translational, and clinical validation. -The current deterministic implementation focuses on anti-PD-1-resistant melanoma. Single-cell/spatial integration, clinical-response modelling and knowledge-graph inference remain future work. +## Data governance -All generated hypotheses require independent experimental, translational and clinical validation. +The current workflow uses public data and curated public-domain biological knowledge, principally Open Targets Platform and DepMap Public releases. No confidential, proprietary, company-internal, or identifiable patient data is included. Source matrices, generated databases, local caches, and complete run directories remain outside version control; only portable, sanitized release evidence and checksums are committed. ## Citation - ```text Soler Ortuño R. TargetIntel-IO: Explainable therapeutic-intent-aware target intelligence for anti-PD-1-resistant melanoma. ``` ## Author - **Rafael Soler Ortuño, PhD** -Computational biologist working across immuno-oncology, biomarker discovery, patient stratification, multi-omics, single-cell and spatial transcriptomics, scientific software engineering and AI-assisted drug discovery. +Computational biologist working across immuno-oncology, biomarker discovery, patient stratification, multi-omics, single-cell and spatial transcriptomics, scientific software engineering, and AI-assisted drug discovery. [LinkedIn](https://www.linkedin.com/in/rafael-soler-ortuno/) ## License Released under the [MIT License](LICENSE). - -## License - -Released under the [MIT License](LICENSE).