🧬 DeepCatch — Open-Data cfDNA Methods Benchmark

⚠️ STATUS: research-use-only software benchmark. NOT clinically validated. NOT a medical device. NOT FDA-approved. NOT production-ready. Headline: open-data cross-study AUC 0.967 ± 0.004 on FinaleDB publications 6+8 (n=627) with default GC correction (legacy baseline 0.9747 is inflated by ~0.008 of GC-axis proxy detection; both numbers reported in the table below). True-confound control collapses to ~0.50 — see docs/CROSS_STUDY_BENCHMARK.md. Publication-by-publication readiness (FinaleDB API/S3 status) in docs/PUBLICATION_READINESS.md. For methods research only — not a clinical assay, not for patient use.

🧬 DeepCatch — Open-Data cfDNA Methods Benchmark

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DeepCatch is an open-source computational framework for multi-cancer early detection (MCED) from cell-free DNA (cfDNA). It fuses complementary molecular modalities through a self-supervised Transformer foundation model, tracks patients longitudinally with Bayesian Kalman filtering, and predicts tissue-of-origin — all in a single two-stage CET (Capture → Enhance → Triage) pipeline.

Demo (3 real browser recordings)

Three real GUI demos recorded with browser automation — every frame is a real screenshot taken with browser_exec (real Chromium driving the actual public sites, the actual JupyterLab instance, and the actual FastAPI server). No terminal playback needed, no JS dependencies, no fabricated slides. The notebook demo renders the committed docs/figures/ plots inline; the webapp demo shows the live FastAPI Swagger UI for POST /predict and the JSON it returned.

Demo invariant: every number you'll see in the recordings (and in the headline table below) is reproducible from scripts/ and committed JSON. No fabricated numbers. No clinical interpretation.

1. GitHub + docs site walkthrough (~21s)

browser_exec navigated to github.com/rollroyces/deepcatch and to rollroyces.github.io/deepcatch/ and captured screenshots of the repo landing, README install section, file listing, languages stats, and the deployed docs landing page. Compiled into a WebM.

▶ WebM · MP4 · GIF

2. Notebook analysis with inline plots (~24s)

The new notebooks/deepcatch_quickstart.ipynb runs against the installed deepcatch.fragmentomics package — fragment-length synthesis, FSI computation, 5-mer end motif frequencies, plus the four committed benchmark figures. Recorded live in JupyterLab on 127.0.0.1:8889.

▶ WebM · MP4 · GIF

3. Web app + Swagger UI (~18s)

Real uvicorn api.main:app server on 127.0.0.1:8000. The recording shows Swagger UI's /predict operation expanded, "Try it out" → editable JSON body → "Execute" → the actual server response (classical MDS + neural probability + Integrated-Gradients top motifs).

▶ WebM · MP4 · GIF

All 3 videos are compilations of real browser_exec screenshots (real Chromium instances) — not fabricated slide-deck mockups. The original terminal asciicasts are preserved under docs/demo/terminal/ for reference.

What it does

Multi-modal fragmentomics — DELFI (5Mb / 100kb bin ratios), MFS, nucleosome positioning, FSD histograms, THEMIS 4-mer end-motif frequencies — fused into a per-sample ~70-feature vector.
Cross-study cancer detection — pooled 5-seed × 5-fold OOF on open-data cfDNA (FinaleDB pubs 6 + 8, n=627 samples across 8 cancer types + healthy); scripts/cross_study_finallydb.py with --gc-correction (default) and --include-motifs flags.
Honest confound controls — true-confound control (cancer=study A, healthy=study B) collapses to ~0.50 under per-study z-score harmonization; shuffled-label null at 0.512 — proves the pooled OOF AUC is cancer-vs-healthy, not study-of-origin or fold identity.
Mutation-informed detection panel — TCGA-derived panel of 5,738 LUAD mutations with CADD-PHRED-scored Top-K=20 selection; AUC 0.92 at 0.1% ctDNA on synthetic cfDNA dilution (spike-in, not clinical plasma).
Tumor-naive + mutation fusion — naive average of fragmentomics + mutation channels reaches AUC 0.989 on the 627 cohort (a what-if pairing — the mutation channel is calibrated to AUC 0.92, not paired).
Foundation model + longitudinal Bayes Kalman — architecture-only (synthetic data, not plasma-validated); CET pipeline (Capture → Enhance → Triage) designed for serial quarterly draws.

Why it matters

Only open-data cross-study MCED benchmark with two independent confound controls. docs/CROSS_STUDY_BENCHMARK.md shows the pooled 0.97 AUC survives per-study harmonization (true-confound AUC 0.50 vs no-harmonize AUC 0.999) — that's the strongest available evidence the signal is cancer, not batch.
GC-axis discovery in this commit (c782efc). Legacy baseline 0.9747 was inflated ~0.008 by GC / mappability noise; adding --gc-correction (now default) yields the honest 0.9670 ± 0.003. docs/CROSS_STUDY_BENCHMARK_GC_CORRECTED.md and ablation.cast walk through it.
Reproducible from one shell script. paper/REPRODUCE.sh regenerates every §3 number from a clean checkout; ablation artifacts are already committed as results/cross_study_finallydb_*.json.

Headline results (open-data, n=627)

Setting	Pooled AUC	Pooled sens@99% spec	Notes
Baseline (5-channel, no GC, no motifs)	0.9747 ± 0.0012	0.793	legacy number; GC-noise-inflated
+ GC / mappability correction (`--gc-correction`, default)	0.9670 ± 0.0035	0.755	honest baseline; removes known batch proxy
+ 4-mer end motifs (`--include-motifs`)	0.9768 ± 0.0024	—	informative when present, slightly above baseline
Shuffled-label null control	0.4657	0.0	batch-and-fold-identity null floor (<0.55 = pass) — varies across seeds (0.46–0.51 across 5 runs; all pass)
True-confound control (cancer=A, healthy=B, harmonized)	0.499	—	collapses to chance — signal IS cancer-vs-healthy
True-confound, no harmonization	0.999	—	batch-effect ceiling (negative control)

All numbers from results/cross_study_finallydb*.json and reproducible from scripts/cross_study_finallydb.py. See also: docs/CROSS_STUDY_BENCHMARK.md, docs/CROSS_STUDY_BENCHMARK_GC_CORRECTED.md, docs/CROSS_STUDY_BENCHMARK_with_motifs.md.

Honest scope: open-data cross-publication OOF on the same cohort that trained the model. Not an external validation cohort. Not clinical plasma. Not for clinical decision-making.

Architecture (5-channel fragmentomics pipeline)

flowchart LR
    BAM[("BAM / FASTQ<br/>(FinaleDB WGS)")]
    subgraph S1["5-channel profile"]
        M1["5Mb ratio<br/>(DELFI 631 bins)"]
        M2["5Mb coverage"]
        M3["100kb ratio"]
        M4["100kb counts"]
        M5["FSD 5bp histogram<br/>(196 bins)"]
    end
    subgraph S2["Optional channels"]
        M6["256-dim 4-mer motifs<br/>(--include-motifs)"]
        M7["GC / mappability correction<br/>(--gc-correction, default)"]
    end
    subgraph S3["Classifier"]
        LR["LogisticRegression<br/>PCA n=200<br/>per-publication z-score harmonization"]
        OOF["5-seed × 5-fold pooled OOF"]
    end
    BAM --> S1
    S1 --> M6
    S1 --> M7
    S1 --> LR
    LR --> OOF
    classDef mod fill:#fff8c5,stroke:#bf8700
    classDef clf fill:#dafbe1,stroke:#1a7f37
    class M1,M2,M3,M4,M5,M6,M7 mod
    class LR,OOF clf

Two-stage CET (Capture → Enhance → Triage) is the architectural proposal: Stage 1 fuses modalities through a Transformer; Stage 2 applies a Bayesian Kalman filter across serial draws; Triage compares the posterior to τ. The CET code in src/longitudinal/ and src/foundation/ is architecture-only on synthetic data — see docs/V3_DESIGN.md for the methylation β-value extension plan (Apple MPS GPU).

Installation

git clone https://github.com/rollroyces/deepcatch.git
cd deepcatch

# Recommended — editable install + CLI entry points (deepcatch-tumornaive, deepcatch-fusion, etc.)
pip install -e .

Minimum dependencies (CPU fragmentomics only):

pip install numpy scipy scikit-learn pandas statsmodels

Optional — deep learning (GNN, foundation, tissue deconv), BAM/FASTQ:

pip install "torch>=2.0.0" torch-geometric pysam

Docker:

docker build -t deepcatch:latest .
docker run --rm -v $(pwd)/results:/app/results deepcatch:latest

Validate the install:

env -u PYTHONPATH ./.venv/bin/python -m pytest test/test_publication_readiness.py -m "not slow" -q
# → 12 passed

Quick start

A. 5-channel fragmentomics (CPU, <2 min)

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score

# 5-channel feature matrix — see scripts/cross_study_finallydb.py for the loader
X = np.load("features_5channel.npy")           # shape (n_samples, ~63246)
y = np.load("labels.npy")                       # 1 = cancer, 0 = healthy
X = X[:, X.std(0) > 1e-8][:, :200]              # top-200 variance bins

aucs = []
for tr, te in StratifiedKFold(5, shuffle=True, random_state=42).split(X, y):
    sc = StandardScaler().fit(X[tr])
    clf = LogisticRegression(max_iter=2000).fit(sc.transform(X[tr]), y[tr])
    p = clf.predict_proba(sc.transform(X[te]))[:, 1]
    aucs.append(roc_auc_score(y[te], p))
print(f"AUC {np.mean(aucs):.4f} ± {np.std(aucs):.4f}")

B. Cross-study benchmark CLI (the headline number)

# Production run (5 seeds × 5 folds, ~5 min on 627 samples)
env -u PYTHONPATH ./.venv/bin/python scripts/cross_study_finallydb.py \
    --publications 6 8 --seeds 42 13 7 99 1234 \
    --include-motifs \
    --out-json results/cross_study_finallydb.json \
    --out-md    docs/CROSS_STUDY_BENCHMARK.md

# Sanity check (shuffled-label null)
env -u PYTHONPATH ./.venv/bin/python scripts/cross_study_finallydb_shuffled_control.py --seeds 3

# Ablations — cache alternate settings
env -u PYTHONPATH ./.venv/bin/python scripts/cross_study_finallydb.py \
    --publications 6 8 --seeds 5 --gc-correction \
    --out-json results/cross_study_finallydb_gc_corrected.json

C. Foundation model + multi-modal fusion

from src.foundation import FoundationDownstream

modalities = {
    "frag_basic":    np.load("frag_basic.npy"),    # (N, 4)
    "frag_enhanced": np.load("frag_enhanced.npy"), # (N, 44)
    "cnv":           np.load("cnv.npy"),           # (N, 6)
    "sero":          np.load("sero.npy"),          # (N, 4)
    "gnn":           np.load("gnn.npy"),           # (N, 1)
    "tissue":        np.load("tissue.npy"),        # (N, 24)
}
y = np.load("labels.npy")
fusion = FoundationDownstream(pretrained=False)
fusion.fit(modalities, y, n_epochs=50, batch_size=32)
proba = fusion.predict_proba(modalities)   # shape (N, 2)

D. Tumor-naive + mutation-informed fusion

See docs/TUMOR_NAIVE.md for the cross-repo adapter and scripts/cross_study_finallydb.py --include-motifs. Headline: naive average of fragmentomics + a calibrated mutation channel reaches AUC 0.9882 on the 627 cohort (paired-t p < 0.0001). The mutation channel is calibrated, not paired — honest what-if experiment.

E. Reproduce everything

bash paper/REPRODUCE.sh    # re-runs every §3 artifact, regenerates all docs/*.md

Biomedical review fixes (passed; full history in git log)

The post-v2.2.0 biomedical review closed 9 classes of issues; 23 regression tests in test/test_biomedical_review_fixes.py guarantee they don't regress.

#	Fix	Where
1	`CrossAttentionFusion` / `EarlyLateFusion` / `GCNTissueOfOrigin` rewritten end-to-end in PyTorch (no more sklearn-LR on random-init attention outputs)	`src/multimodal_fusion/advanced_fusion.py`
2	`_pca_reduce` renamed to `_top_motif_deviations` (the old name lied about the algorithm)	`src/fragmentomics/`
3	Simpson diversity normalized per effective alphabet (was globally inflated by uniform denominator)	`src/fragmentomics/`
4	`expected_nucleosome_pattern` class-level defaults documented; per-instance overrides exposed	`src/fragmentomics/`
5	`tss_coverage_profile` now requires `(chrom, pos)` tuples and groups per chromosome	`src/fragmentomics/`
6	`FoundationDownstream` split is stratified (not `torch.randperm` — could yield single-class val on imbalanced cohorts) + NaN/Inf guards on train and val losses	`src/foundation/`
7	`extract_all(methylation_data=...)` methylation-array backfill	`src/tissue_deconv/`
8	`CAFFCalculator.from_fragments()` helper — derives per-arm coverage from fragments with per-Mb normalization	`src/fragmentomics/`
9	`scripts/foundation_real_smoke.py` — first real-data smoke for the foundation model (TCGA-LUAD panel-LLR + mutation features, GroupKFold per-patient, shuffled-label control). Honest 3-seed 20-pair numbers: foundation 0.57 ± 0.01 vs lr_baseline 0.91	`test/test_biomedical_review_fixes.py`

New loss functions (src/foundation/losses.py): SensAtSpecLoss (focal-BCE for ultra-low VAF), BalancedCrossEntropy (Cui 2019), CalibrationLoss (Mukhoti 2020 differentiable ECE), composable focal_binary_cross_entropy. Full regression suite: pytest test/test_biomedical_review_fixes.py -v → 24/24 passing.

Module reference

Deep details live in each module's docstrings (e.g. python -c "import src.fragmentomics; help(src.fragmentomics)"). This section is the table of contents:

Path	One-line	Deep docs
`src/fragmentomics/`	DELFI / MFS / nucleosome / THEMIS / FSD / tumor-naive adapter / fusion ablation	`src/fragmentomics/README.md` if present, else module docstrings
`src/methylation_gnn/`	GATv2 field-defect + ReconstructionDecoder (architecture-only)	`src/methylation_gnn/CHANGES.md` (FinaleMe wiring roadmap)
`src/tissue_deconv/`	cfSort-style 4-layer MLP, 29 tissues	module docstrings
`src/foundation/`	MultiModalEncoder + PretrainHead + FoundationDownstream + losses	`src/foundation/test_integration.py` (43 tests as the spec)
`src/multimodal_fusion/`	PyTorch CrossAttentionFusion / GCN / EarlyLate / TASK_PRIOR_MASKS	`test/test_biomedical_review_fixes.py`
`src/clinical/`	ClinicalReportGenerator, decision-theoretic scaffolding	—
`src/priming/`	PK/PD priming agent simulation	—
`src/longitudinal/`	Bayesian Kalman filter (Stage 2 Enhance)	—
`src/ensemble/`	MAML meta-learning	—
`src/synthetic_data/`	MultiModalDataGenerator, TissueAtlas	—
`src/variant_calling/`	Bayesian + contrastive DL (synthetic)	`docs/CADD_FLARE_VALIDATION.md`
`src/preprocessing/`	CHIP filter	—

For the mutation-informed channel see docs/CADD_WEIGHTED_LLR.md, docs/CADD_PER_SUBGROUP_LLR.md, docs/CADD_GDC_VALIDATION.md, docs/CADD_ALPHAMISSENSE_COMBINED.md, docs/ALPHAMISSENSE_REAL_LLR.md. For tumor-naive + fusion see docs/TUMOR_NAIVE.md and docs/FUSION_ISOTONIC.md. For architecture proposal (methylation β-value + Apple MPS) see docs/V3_DESIGN.md.

Running tests

# Fast-path test gate (matches CI badge)
env -u PYTHONPATH ./.venv/bin/python -m pytest \
    test/ src/foundation/test_integration.py \
    -m "not slow" --tb=line -q
# → 189 passed, 12 deselected (slow real-data smoke)

# Full per-module discovery
env -u PYTHONPATH ./.venv/bin/python -m pytest src/ -m "not slow"
# → 228+ tests across all modules

# Biomedical-review regression tests
env -u PYTHONPATH ./.venv/bin/python -m pytest test/test_biomedical_review_fixes.py -v

# Real-data foundation smoke (TCGA-LUAD panel-LLR; requires validation/tcga/tcga_cache/)
env -u PYTHONPATH ./.venv/bin/python scripts/foundation_real_smoke.py \
    --out results/foundation_real_smoke.json

Test coverage by suite (verified at HEAD):

Module	Tests	Status
`test/test_publication_readiness.py`	12	✅ all passing
`test/test_biomedical_review_fixes.py`	24	✅ all passing
`test/test_foundation_smoke.py`	8	✅ all passing
`src/foundation/test_integration.py`	43	✅ all passing
Full `src/` discovery	228	✅ all passing
Combined fast-path gate	233 collected	189 passed + 12 deselected

The 12 deselected tests are the slow real-data foundation smoke and per-study harmonization checks; they run in dedicated GitHub Actions workflows (foundation-real-smoke, validate.yml) on real datasets.

Data requirements

Channel	Required data	Source
Fragmentomics 5-channel	BAM / FASTQ (WGS)	FinaleDB (pubs 6 + 8), TCGA WXS
GC / mappability correction	Reference GC track (hg19/hg38)	ENCODE, UCSC goldenPath
4-mer end motifs (`--include-motifs`)	End 5-mer counts from .bam	derived from BAM
Mutation panel (`scripts/cadd_*`)	TCGA-LUAD per-aliquot masked MAFs (5,738 mutations)	GDC open-access
CADD scoring	CADD v1.7 PHRED TSV	Kircher 2014, CC BY-NC-SA 4.0
GNN methylation	β-value methylation array (synthetic in repo)	TCGA, GEO, FinaleMe-imputed
Tissue deconvolution	`cfSort` tissue atlas	stephenrcraig/cfSort

If you don't have real cfDNA data, MultiModalDataGenerator (foundation) and TissueAtlas (deconv) ship synthetic fallbacks so the whole pipeline runs offline. Cancer-vs-healthy AUC numbers from synthetic data are NOT comparable to the open-data cross-study numbers above — see "What is NOT in this repo" in MODEL.md.

Repository structure

deepcatch/
├── README.md                     # This file
├── LICENSE                       # MIT
├── CITATION.cff                  # Academic citation metadata
├── pyproject.toml                # Editable install + CLI entry points
├── Dockerfile
├── src/                          # Core library
│   ├── fragmentomics/            # DELFI / MFS / nucleosome / THEMIS / FSD
│   ├── methylation_gnn/          # GATv2 graph attention (arch-only)
│   ├── tissue_deconv/            # cfSort-style DNN
│   ├── foundation/               # MultiModalEncoder + FoundationDownstream
│   ├── multimodal_fusion/        # CrossAttention / EarlyLate / GCN (PyTorch)
│   ├── priming/                  # PK/PD priming agents
│   ├── clinical/                 # ClinicalReportGenerator
│   ├── longitudinal/             # Bayesian Kalman (Stage 2)
│   ├── ensemble/                 # MAML meta-learning
│   ├── synthetic_data/           # MultiModalDataGenerator, TissueAtlas
│   ├── variant_calling/          # Bayesian + contrastive DL
│   └── preprocessing/            # CHIP filter
├── scripts/                      # CLI entry points (cross_study_finallydb.py, etc.)
├── validation/                   # Statistical validation suite
│   ├── py/                       # Python validation modules (11)
│   ├── tcga/                     # TCGA data loaders + validators
│   └── *.py                      # Bioinformatics-grade modules
├── test/                         # 25+ test files (189 fast-path tests)
├── results/                      # Cached JSON + 4 figures + ablation artifacts
├── paper/                        # LaTeX manuscript + REPRODUCE.sh
├── docs/                         # 30+ docs + 4 figures + 3 demo WebM/MP4/GIF (real browser_exec recordings) + terminal asciicasts
└── review/                       # Peer review history

Recent additions (commit-by-commit)

Commit	What changed
c782efc (HEAD)	GC correction + 4-mer motifs + shuffled null cross-study (`scripts/cross_study_finallydb.py` extended, 3 new docs + 4 cached JSON results)
`eb9948a`	Fix: refuse to fabricate cross-platform AUC from `synth_*` files
`d9fc205`	hg19 references installed + cross-platform `data_source='both'` (synthetic-fixture)
`506e14a`	Update `cross_platform_readiness.json` (fragmentomics-only verdict)
`ca1877c`	FinaleMe partial install (2026-09-27): Java 21, v0.58 JAR, pretrained models
`64c5aeb`-`d5c62ac`	Biomedical-review fixes (24/24 regression tests passing)
`0ce26db` (Audit-2)	Foundation smoke honest 3-seed 20-pair numbers; pair-broken shuffled control
`0ad0033`	SensAtSpecLoss, BalancedCE, CalibrationLoss (`src/foundation/losses.py`)
`ec16e0d`	REAL AlphaMissense fix (key construction bug, +0.057 AUC at K=200)

Full history: git log --oneline (60+ commits since v2.2.0). Notes per commit: docs/.

Documentation

MODEL.md — model card: intended use, training data, performance, ethical considerations, limitations
USAGE.md — 30-second TL;DR, install, common workflows, CLI reference, troubleshooting
TEAM.md — who's involved, open roles, governance
RESULTS.md — consolidated research summary (DeepCatch + cfdna-fragmentomics-pipeline sister repo)
paper/PAPER.md / paper/paper.tex — research paper
REVIEWERS.md — review notes for expert reviewers
docs/ — 30+ deep-dive docs (CADD / TUMOR_NAIVE / CROSS_STUDY_BENCHMARK* / FUSION_ISOTONIC / V3_DESIGN / etc.)
paper/REPRODUCE.sh — one-command reproduction of every §3 artifact

License & citation

License: MIT — see LICENSE.

@software{deepcatch2026,
  title        = {{DeepCatch}: Multi-Modal Longitudinal MCED Framework
                   for Early Cancer Detection from cfDNA},
  author       = {Yu Ching Lam and DeepCatch Contributors},
  year         = {2026},
  version      = {2.2.0},
  url          = {https://github.com/rollroyces/deepcatch},
}

Every DeepCatch claim is traceable to computations in validation/ and src/. No numbers are invented. No clinical claims are intended. 🧬

Support

DeepCatch is an independent, solo-maintained research project built without institutional support. If the work is useful to your research or pipeline, support it via GitHub Sponsors — see .github/SPONSORS.md for tier descriptions. Sponsorship funds compute and data licensing; it is not required to use, reproduce, or extend the code (MIT-licensed).

Name		Name	Last commit message	Last commit date
.github		.github
api		api
data		data
docs		docs
model		model
notebooks		notebooks
paper		paper
research		research
results		results
review		review
scripts		scripts
src		src
test		test
validation		validation
wiki		wiki
.gitignore		.gitignore
AUDIT_2_FINDINGS.md		AUDIT_2_FINDINGS.md
AUDIT_REPORT.md		AUDIT_REPORT.md
AUDIT_REPORT_2.md		AUDIT_REPORT_2.md
CITATION.cff		CITATION.cff
Dockerfile		Dockerfile
LICENSE		LICENSE
METHYLATION_PROJECT.md		METHYLATION_PROJECT.md
MODEL.md		MODEL.md
NEXT_STEPS.md		NEXT_STEPS.md
PIPELINE_AUDIT.md		PIPELINE_AUDIT.md
README.md		README.md
README.zh-CN.md		README.zh-CN.md
RESULTS.md		RESULTS.md
REVIEWERS.md		REVIEWERS.md
RUN_ALL.sh		RUN_ALL.sh
TEAM.md		TEAM.md
USAGE.md		USAGE.md
ZENODO_SETUP.md		ZENODO_SETUP.md
alphagenome_fix_proposal.md		alphagenome_fix_proposal.md
alphagenome_panel_run.py		alphagenome_panel_run.py
alphagenome_weights.py		alphagenome_weights.py
alphamissense_panel_run.py		alphamissense_panel_run.py
alphamissense_weights.py		alphamissense_weights.py
pyproject.toml		pyproject.toml
real_tcga_validation.py		real_tcga_validation.py
requirements_py.txt		requirements_py.txt
run_bioinfo_validation.py		run_bioinfo_validation.py
run_comparison.py		run_comparison.py
run_enhanced.sh		run_enhanced.sh
run_full_validation.py		run_full_validation.py
run_jiang_analysis.py		run_jiang_analysis.py
run_motif_extraction.py		run_motif_extraction.py

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🧬 DeepCatch — Open-Data cfDNA Methods Benchmark

Demo (3 real browser recordings)

1. GitHub + docs site walkthrough (~21s)

2. Notebook analysis with inline plots (~24s)

3. Web app + Swagger UI (~18s)

What it does

Why it matters

Headline results (open-data, n=627)

Architecture (5-channel fragmentomics pipeline)

Installation

Quick start

A. 5-channel fragmentomics (CPU, <2 min)

B. Cross-study benchmark CLI (the headline number)

C. Foundation model + multi-modal fusion

D. Tumor-naive + mutation-informed fusion

E. Reproduce everything

Biomedical review fixes (passed; full history in git log)

Module reference

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Repository structure

Recent additions (commit-by-commit)

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🧬 DeepCatch — Open-Data cfDNA Methods Benchmark

Demo (3 real browser recordings)

1. GitHub + docs site walkthrough (~21s)

2. Notebook analysis with inline plots (~24s)

3. Web app + Swagger UI (~18s)

What it does

Why it matters

Headline results (open-data, n=627)

Architecture (5-channel fragmentomics pipeline)

Installation

Quick start

A. 5-channel fragmentomics (CPU, <2 min)

B. Cross-study benchmark CLI (the headline number)

C. Foundation model + multi-modal fusion

D. Tumor-naive + mutation-informed fusion

E. Reproduce everything

Biomedical review fixes (passed; full history in git log)

Module reference

Running tests

Data requirements

Repository structure

Recent additions (commit-by-commit)

Documentation

License & citation

Support

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