[{"id":"bf-001-oncovit-lung","name":"OncoViT-Lung","domain":"Cancer Detection","modality":"Imaging","org":"PathAI Community","task":"Lung nodule malignancy classification","metric_name":"AUC","metric_value":0.822,"downloads":240617,"license":"MIT","version":"1.5.2","rating":4.3,"description":"OncoViT-Lung is a SYNTHETIC/DEMO cancer detection model for lung nodule malignancy classification. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"CT scan (DICOM)","outputs":"Malignancy probability + ROI heatmap","connected_ids":["bf-011-thyronod-us","bf-007-pancan-rna","bf-002-mammonet-screen"]},{"id":"bf-002-mammonet-screen","name":"MammoNet-Screen","domain":"Cancer Detection","modality":"Imaging","org":"NeuroScan Labs","task":"Breast cancer screening from mammography","metric_name":"AUC","metric_value":0.939,"downloads":339658,"license":"BioFoundry Research","version":"1.0.2","rating":4.4,"description":"MammoNet-Screen is a SYNTHETIC/DEMO cancer detection model for breast cancer screening from mammography. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Mammogram (2-view)","outputs":"BI-RADS score + lesion map","connected_ids":["bf-001-oncovit-lung","bf-003-dermoscan-mel","bf-008-liquidbiopsy-ctdna"]},{"id":"bf-003-dermoscan-mel","name":"DermoScan-Mel","domain":"Cancer Detection","modality":"Imaging","org":"Genoma Institute","task":"Melanoma vs benign nevus classification","metric_name":"Accuracy","metric_value":0.832,"downloads":272825,"license":"Apache-2.0","version":"3.5.7","rating":4.8,"description":"DermoScan-Mel is a SYNTHETIC/DEMO cancer detection model for melanoma vs benign nevus classification. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Dermoscopy image","outputs":"Melanoma probability","connected_ids":["bf-006-glioseg-brain","bf-007-pancan-rna","bf-005-colopath-detect"]},{"id":"bf-004-prostamri-grade","name":"ProstaMRI-Grade","domain":"Cancer Detection","modality":"Imaging","org":"Insilico BioFoundry","task":"Prostate lesion PI-RADS grading","metric_name":"AUC","metric_value":0.828,"downloads":210473,"license":"Apache-2.0","version":"3.5.0","rating":5.0,"description":"ProstaMRI-Grade is a SYNTHETIC/DEMO cancer detection model for prostate lesion pi-rads grading. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Multiparametric MRI","outputs":"PI-RADS grade + confidence","connected_ids":["bf-010-pancnet-early","bf-008-liquidbiopsy-ctdna","bf-007-pancan-rna"]},{"id":"bf-005-colopath-detect","name":"ColoPath-Detect","domain":"Cancer Detection","modality":"Imaging","org":"EMBL-Synthetic","task":"Colorectal polyp histopathology detection","metric_name":"F1","metric_value":0.95,"downloads":36436,"license":"BSD-3-Clause","version":"4.9.0","rating":4.1,"description":"ColoPath-Detect is a SYNTHETIC/DEMO cancer detection model for colorectal polyp histopathology detection. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"H&E slide tile","outputs":"Polyp class + attention","connected_ids":["bf-006-glioseg-brain","bf-008-liquidbiopsy-ctdna","bf-012-ensembleonco-xl"]},{"id":"bf-006-glioseg-brain","name":"GlioSeg-Brain","domain":"Cancer Detection","modality":"Imaging","org":"BioNexus","task":"Glioma segmentation & grading","metric_name":"Dice","metric_value":0.905,"downloads":158878,"license":"BSD-3-Clause","version":"3.9.3","rating":4.7,"description":"GlioSeg-Brain is a SYNTHETIC/DEMO cancer detection model for glioma segmentation & grading. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Brain MRI (FLAIR/T1c)","outputs":"Tumor mask + grade","connected_ids":["bf-011-thyronod-us","bf-005-colopath-detect","bf-010-pancnet-early"]},{"id":"bf-007-pancan-rna","name":"PanCan-RNA","domain":"Cancer Detection","modality":"Sequence","org":"DeepCell AI","task":"Pan-cancer type from RNA expression","metric_name":"Accuracy","metric_value":0.836,"downloads":119522,"license":"Apache-2.0","version":"2.7.8","rating":4.9,"description":"PanCan-RNA is a SYNTHETIC/DEMO cancer detection model for pan-cancer type from rna expression. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"RNA-seq expression vector","outputs":"Tissue-of-origin + probability","connected_ids":["bf-012-ensembleonco-xl","bf-011-thyronod-us","bf-008-liquidbiopsy-ctdna"]},{"id":"bf-008-liquidbiopsy-ctdna","name":"LiquidBiopsy-ctDNA","domain":"Cancer Detection","modality":"Sequence","org":"Stanford BioML","task":"Circulating tumor DNA variant calling","metric_name":"Sensitivity","metric_value":0.84,"downloads":28101,"license":"BioFoundry Research","version":"1.1.8","rating":4.2,"description":"LiquidBiopsy-ctDNA is a SYNTHETIC/DEMO cancer detection model for circulating tumor dna variant calling. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"cfDNA sequencing reads","outputs":"Variant calls + tumor fraction","connected_ids":["bf-007-pancan-rna","bf-005-colopath-detect","bf-011-thyronod-us"]},{"id":"bf-009-hemeblast-leuk","name":"HemeBlast-Leuk","domain":"Cancer Detection","modality":"Imaging","org":"MayoBio Labs","task":"Acute leukemia blast cell classification","metric_name":"Accuracy","metric_value":0.858,"downloads":248532,"license":"BioFoundry Research","version":"2.4.9","rating":5.0,"description":"HemeBlast-Leuk is a SYNTHETIC/DEMO cancer detection model for acute leukemia blast cell classification. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Blood smear microscopy","outputs":"Blast % + subtype","connected_ids":["bf-010-pancnet-early","bf-012-ensembleonco-xl","bf-005-colopath-detect"]},{"id":"bf-010-pancnet-early","name":"PancNet-Early","domain":"Cancer Detection","modality":"Multi-modal","org":"Genoma Institute","task":"Early pancreatic cancer risk from CT+labs","metric_name":"AUC","metric_value":0.891,"downloads":13658,"license":"MIT","version":"2.7.2","rating":4.6,"description":"PancNet-Early is a SYNTHETIC/DEMO cancer detection model for early pancreatic cancer risk from ct+labs. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified multi-modal data.","inputs":"CT + serum panel","outputs":"Risk score + drivers","connected_ids":["bf-006-glioseg-brain","bf-001-oncovit-lung","bf-009-hemeblast-leuk"]},{"id":"bf-011-thyronod-us","name":"ThyroNod-US","domain":"Cancer Detection","modality":"Imaging","org":"DeepCell AI","task":"Thyroid nodule TI-RADS classification","metric_name":"AUC","metric_value":0.926,"downloads":405191,"license":"CC-BY-4.0","version":"1.1.8","rating":4.5,"description":"ThyroNod-US is a SYNTHETIC/DEMO cancer detection model for thyroid nodule ti-rads classification. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Ultrasound frame","outputs":"TI-RADS category","connected_ids":["bf-004-prostamri-grade","bf-010-pancnet-early","bf-002-mammonet-screen"]},{"id":"bf-012-ensembleonco-xl","name":"EnsembleOnco-XL","domain":"Cancer Detection","modality":"Multi-modal","org":"BioNexus","task":"Multi-organ tumor board ensemble","metric_name":"AUC","metric_value":0.828,"downloads":319425,"license":"BioFoundry Research","version":"4.8.4","rating":4.7,"description":"EnsembleOnco-XL is a SYNTHETIC/DEMO cancer detection model for multi-organ tumor board ensemble. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified multi-modal data.","inputs":"Imaging + pathology + genomics","outputs":"Consensus malignancy + report","connected_ids":["bf-009-hemeblast-leuk","bf-006-glioseg-brain","bf-010-pancnet-early"]},{"id":"bf-013-dockformer-bind","name":"DockFormer-Bind","domain":"Drug Discovery","modality":"Sequence","org":"PathAI Community","task":"Protein-ligand binding affinity prediction","metric_name":"Pearson r","metric_value":0.948,"downloads":399277,"license":"CC-BY-4.0","version":"4.4.3","rating":4.5,"description":"DockFormer-Bind is a SYNTHETIC/DEMO drug discovery model for protein-ligand binding affinity prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"SMILES + protein sequence","outputs":"pKd + pose confidence","connected_ids":["bf-014-molgen-lead","bf-018-repurposenet","bf-015-admet-guard"]},{"id":"bf-014-molgen-lead","name":"MolGen-Lead","domain":"Drug Discovery","modality":"Sequence","org":"Insilico BioFoundry","task":"De novo lead-like molecule generation","metric_name":"Validity","metric_value":0.87,"downloads":7084,"license":"CC-BY-4.0","version":"2.1.4","rating":4.2,"description":"MolGen-Lead is a SYNTHETIC/DEMO drug discovery model for de novo lead-like molecule generation. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Target profile","outputs":"Candidate SMILES set","connected_ids":["bf-016-kinasehit-screen","bf-013-dockformer-bind","bf-015-admet-guard"]},{"id":"bf-015-admet-guard","name":"ADMET-Guard","domain":"Drug Discovery","modality":"Tabular","org":"BioNexus","task":"ADMET property + toxicity prediction","metric_name":"ROC-AUC","metric_value":0.916,"downloads":301640,"license":"BioFoundry Research","version":"1.5.4","rating":3.8,"description":"ADMET-Guard is a SYNTHETIC/DEMO drug discovery model for admet property + toxicity prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified tabular data.","inputs":"Molecular descriptors","outputs":"ADMET panel + tox flags","connected_ids":["bf-017-synthroute-plan","bf-016-kinasehit-screen","bf-013-dockformer-bind"]},{"id":"bf-016-kinasehit-screen","name":"KinaseHit-Screen","domain":"Drug Discovery","modality":"Sequence","org":"BroadOpen Collective","task":"Kinase inhibitor virtual screening","metric_name":"Enrichment","metric_value":0.968,"downloads":291314,"license":"MIT","version":"2.9.7","rating":4.0,"description":"KinaseHit-Screen is a SYNTHETIC/DEMO drug discovery model for kinase inhibitor virtual screening. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Compound library","outputs":"Ranked hits + scores","connected_ids":["bf-014-molgen-lead","bf-018-repurposenet","bf-013-dockformer-bind"]},{"id":"bf-017-synthroute-plan","name":"SynthRoute-Plan","domain":"Drug Discovery","modality":"Sequence","org":"PathAI Community","task":"Retrosynthesis route planning","metric_name":"Top-1 acc","metric_value":0.836,"downloads":26464,"license":"CC-BY-4.0","version":"3.0.9","rating":4.6,"description":"SynthRoute-Plan is a SYNTHETIC/DEMO drug discovery model for retrosynthesis route planning. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Target molecule","outputs":"Synthesis tree","connected_ids":["bf-016-kinasehit-screen","bf-014-molgen-lead","bf-013-dockformer-bind"]},{"id":"bf-018-repurposenet","name":"RepurposeNet","domain":"Drug Discovery","modality":"Multi-modal","org":"Helix Diagnostics","task":"Drug repurposing from knowledge graph","metric_name":"MRR","metric_value":0.87,"downloads":180912,"license":"BSD-3-Clause","version":"2.1.3","rating":4.3,"description":"RepurposeNet is a SYNTHETIC/DEMO drug discovery model for drug repurposing from knowledge graph. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified multi-modal data.","inputs":"Disease + drug graph","outputs":"Ranked repurposing candidates","connected_ids":["bf-017-synthroute-plan","bf-014-molgen-lead","bf-015-admet-guard"]},{"id":"bf-019-foldopen-mini","name":"FoldOpen-Mini","domain":"Protein Folding","modality":"Sequence","org":"Genoma Institute","task":"Single-chain structure prediction","metric_name":"GDT-TS","metric_value":0.821,"downloads":101539,"license":"CC-BY-4.0","version":"2.5.3","rating":4.5,"description":"FoldOpen-Mini is a SYNTHETIC/DEMO protein folding model for single-chain structure prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Amino-acid sequence","outputs":"3D coordinates + pLDDT","connected_ids":["bf-024-membranefold-tm","bf-023-epitopemap-b","bf-021-mutstab-ddg"]},{"id":"bf-020-complexfold-ppi","name":"ComplexFold-PPI","domain":"Protein Folding","modality":"Sequence","org":"PathAI Community","task":"Protein-protein complex prediction","metric_name":"DockQ","metric_value":0.926,"downloads":115459,"license":"BSD-3-Clause","version":"4.2.5","rating":4.7,"description":"ComplexFold-PPI is a SYNTHETIC/DEMO protein folding model for protein-protein complex prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Two chain sequences","outputs":"Complex structure + interface","connected_ids":["bf-019-foldopen-mini","bf-022-disorderpred","bf-021-mutstab-ddg"]},{"id":"bf-021-mutstab-ddg","name":"MutStab-ddG","domain":"Protein Folding","modality":"Sequence","org":"Helix Diagnostics","task":"Mutation stability (ddG) prediction","metric_name":"RMSE","metric_value":1.5,"downloads":197574,"license":"CC-BY-4.0","version":"3.5.9","rating":5.0,"description":"MutStab-ddG is a SYNTHETIC/DEMO protein folding model for mutation stability (ddg) prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Sequence + mutation","outputs":"ddG kcal/mol","connected_ids":["bf-022-disorderpred","bf-023-epitopemap-b","bf-024-membranefold-tm"]},{"id":"bf-022-disorderpred","name":"DisorderPred","domain":"Protein Folding","modality":"Sequence","org":"BroadOpen Collective","task":"Intrinsically disordered region prediction","metric_name":"AUC","metric_value":0.845,"downloads":83002,"license":"MIT","version":"3.9.7","rating":4.6,"description":"DisorderPred is a SYNTHETIC/DEMO protein folding model for intrinsically disordered region prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Amino-acid sequence","outputs":"Per-residue disorder score","connected_ids":["bf-023-epitopemap-b","bf-020-complexfold-ppi","bf-021-mutstab-ddg"]},{"id":"bf-023-epitopemap-b","name":"EpitopeMap-B","domain":"Protein Folding","modality":"Sequence","org":"BroadOpen Collective","task":"B-cell epitope prediction","metric_name":"F1","metric_value":0.888,"downloads":46297,"license":"BSD-3-Clause","version":"4.5.1","rating":4.1,"description":"EpitopeMap-B is a SYNTHETIC/DEMO protein folding model for b-cell epitope prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Antigen sequence","outputs":"Epitope regions","connected_ids":["bf-024-membranefold-tm","bf-022-disorderpred","bf-019-foldopen-mini"]},{"id":"bf-024-membranefold-tm","name":"MembraneFold-TM","domain":"Protein Folding","modality":"Sequence","org":"DeepCell AI","task":"Transmembrane topology prediction","metric_name":"Q3","metric_value":0.825,"downloads":418401,"license":"CC-BY-4.0","version":"3.4.6","rating":3.9,"description":"MembraneFold-TM is a SYNTHETIC/DEMO protein folding model for transmembrane topology prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Sequence","outputs":"Topology + helices","connected_ids":["bf-020-complexfold-ppi","bf-019-foldopen-mini","bf-023-epitopemap-b"]},{"id":"bf-025-varpath-clin","name":"VarPath-Clin","domain":"Genomics","modality":"Sequence","org":"PathAI Community","task":"Clinical variant pathogenicity classification","metric_name":"AUC","metric_value":0.976,"downloads":131078,"license":"Apache-2.0","version":"3.2.1","rating":4.3,"description":"VarPath-Clin is a SYNTHETIC/DEMO genomics model for clinical variant pathogenicity classification. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"VCF variant record","outputs":"ACMG class + score","connected_ids":["bf-030-crispr-guide","bf-028-scrna-annotate","bf-026-splicedeep"]},{"id":"bf-026-splicedeep","name":"SpliceDeep","domain":"Genomics","modality":"Sequence","org":"Genoma Institute","task":"Splice-site & aberrant splicing prediction","metric_name":"PR-AUC","metric_value":0.887,"downloads":30350,"license":"MIT","version":"4.5.9","rating":3.9,"description":"SpliceDeep is a SYNTHETIC/DEMO genomics model for splice-site & aberrant splicing prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Genomic sequence window","outputs":"Splice delta score","connected_ids":["bf-030-crispr-guide","bf-029-enhancerfinder","bf-028-scrna-annotate"]},{"id":"bf-027-polyrisk-score","name":"PolyRisk-Score","domain":"Genomics","modality":"Tabular","org":"DeepCell AI","task":"Polygenic risk score estimation","metric_name":"AUC","metric_value":0.836,"downloads":420110,"license":"CC-BY-4.0","version":"4.8.3","rating":4.6,"description":"PolyRisk-Score is a SYNTHETIC/DEMO genomics model for polygenic risk score estimation. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified tabular data.","inputs":"Genotype array","outputs":"Trait PRS + percentile","connected_ids":["bf-026-splicedeep","bf-025-varpath-clin","bf-030-crispr-guide"]},{"id":"bf-028-scrna-annotate","name":"scRNA-Annotate","domain":"Genomics","modality":"Sequence","org":"Helix Diagnostics","task":"Single-cell type annotation","metric_name":"Accuracy","metric_value":0.935,"downloads":265579,"license":"Apache-2.0","version":"1.7.3","rating":4.5,"description":"scRNA-Annotate is a SYNTHETIC/DEMO genomics model for single-cell type annotation. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"scRNA-seq matrix","outputs":"Cell-type labels","connected_ids":["bf-025-varpath-clin","bf-029-enhancerfinder","bf-030-crispr-guide"]},{"id":"bf-029-enhancerfinder","name":"EnhancerFinder","domain":"Genomics","modality":"Sequence","org":"Helix Diagnostics","task":"Regulatory enhancer prediction","metric_name":"AUPRC","metric_value":0.943,"downloads":74745,"license":"Apache-2.0","version":"1.3.8","rating":4.9,"description":"EnhancerFinder is a SYNTHETIC/DEMO genomics model for regulatory enhancer prediction. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"DNA sequence","outputs":"Enhancer probability","connected_ids":["bf-025-varpath-clin","bf-026-splicedeep","bf-028-scrna-annotate"]},{"id":"bf-030-crispr-guide","name":"CRISPR-Guide","domain":"Genomics","modality":"Sequence","org":"NeuroScan Labs","task":"gRNA on/off-target efficiency","metric_name":"Spearman","metric_value":0.849,"downloads":456666,"license":"Apache-2.0","version":"1.3.2","rating":4.9,"description":"CRISPR-Guide is a SYNTHETIC/DEMO genomics model for grna on/off-target efficiency. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Target locus + gRNA","outputs":"Efficiency + off-target risk","connected_ids":["bf-028-scrna-annotate","bf-029-enhancerfinder","bf-027-polyrisk-score"]},{"id":"bf-031-hemecount-cbc","name":"HemeCount-CBC","domain":"Blood Analysis","modality":"Tabular","org":"EMBL-Synthetic","task":"Anomaly detection in complete blood count","metric_name":"F1","metric_value":0.821,"downloads":367608,"license":"CC-BY-4.0","version":"4.0.2","rating":4.7,"description":"HemeCount-CBC is a SYNTHETIC/DEMO blood analysis model for anomaly detection in complete blood count. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified tabular data.","inputs":"CBC panel","outputs":"Flagged abnormalities","connected_ids":["bf-034-smearmorph","bf-033-coagpredict","bf-032-sepsisearly-lab"]},{"id":"bf-032-sepsisearly-lab","name":"SepsisEarly-Lab","domain":"Blood Analysis","modality":"Tabular","org":"Helix Diagnostics","task":"Early sepsis risk from serial labs","metric_name":"AUROC","metric_value":0.896,"downloads":305907,"license":"MIT","version":"3.3.2","rating":4.6,"description":"SepsisEarly-Lab is a SYNTHETIC/DEMO blood analysis model for early sepsis risk from serial labs. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified tabular data.","inputs":"Time-series vitals + labs","outputs":"Sepsis risk trajectory","connected_ids":["bf-031-hemecount-cbc","bf-035-biomarkerpanel","bf-033-coagpredict"]},{"id":"bf-033-coagpredict","name":"CoagPredict","domain":"Blood Analysis","modality":"Tabular","org":"NeuroScan Labs","task":"Coagulation disorder screening","metric_name":"AUC","metric_value":0.907,"downloads":51614,"license":"BSD-3-Clause","version":"1.8.1","rating":4.8,"description":"CoagPredict is a SYNTHETIC/DEMO blood analysis model for coagulation disorder screening. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified tabular data.","inputs":"Coag panel","outputs":"Disorder likelihood","connected_ids":["bf-035-biomarkerpanel","bf-031-hemecount-cbc","bf-034-smearmorph"]},{"id":"bf-034-smearmorph","name":"SmearMorph","domain":"Blood Analysis","modality":"Imaging","org":"BioNexus","task":"Peripheral smear morphology classification","metric_name":"Accuracy","metric_value":0.966,"downloads":71684,"license":"Apache-2.0","version":"3.6.4","rating":4.3,"description":"SmearMorph is a SYNTHETIC/DEMO blood analysis model for peripheral smear morphology classification. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Blood smear image","outputs":"Cell morphology labels","connected_ids":["bf-035-biomarkerpanel","bf-031-hemecount-cbc","bf-032-sepsisearly-lab"]},{"id":"bf-035-biomarkerpanel","name":"BioMarkerPanel","domain":"Blood Analysis","modality":"Multi-modal","org":"Helix Diagnostics","task":"Multi-analyte biomarker risk fusion","metric_name":"C-index","metric_value":0.82,"downloads":107205,"license":"BioFoundry Research","version":"4.2.3","rating":3.8,"description":"BioMarkerPanel is a SYNTHETIC/DEMO blood analysis model for multi-analyte biomarker risk fusion. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified multi-modal data.","inputs":"Serum panel + demographics","outputs":"Composite risk","connected_ids":["bf-031-hemecount-cbc","bf-032-sepsisearly-lab","bf-033-coagpredict"]},{"id":"bf-036-chestx-multi","name":"ChestX-Multi","domain":"Medical Imaging","modality":"Imaging","org":"BioNexus","task":"Multi-label chest X-ray finding detection","metric_name":"Mean AUC","metric_value":0.934,"downloads":18641,"license":"BioFoundry Research","version":"4.1.4","rating":4.6,"description":"ChestX-Multi is a SYNTHETIC/DEMO medical imaging model for multi-label chest x-ray finding detection. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Chest radiograph","outputs":"14 finding probabilities","connected_ids":["bf-037-retinascan-dr","bf-039-strokect-triage","bf-038-boneage-assess"]},{"id":"bf-037-retinascan-dr","name":"RetinaScan-DR","domain":"Medical Imaging","modality":"Imaging","org":"Insilico BioFoundry","task":"Diabetic retinopathy grading","metric_name":"Quad-Kappa","metric_value":0.83,"downloads":11704,"license":"MIT","version":"1.5.0","rating":4.4,"description":"RetinaScan-DR is a SYNTHETIC/DEMO medical imaging model for diabetic retinopathy grading. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Fundus photograph","outputs":"DR severity grade","connected_ids":["bf-038-boneage-assess","bf-039-strokect-triage","bf-036-chestx-multi"]},{"id":"bf-038-boneage-assess","name":"BoneAge-Assess","domain":"Medical Imaging","modality":"Imaging","org":"Genoma Institute","task":"Pediatric bone-age estimation","metric_name":"MAE (mo)","metric_value":7.0,"downloads":145373,"license":"BioFoundry Research","version":"4.2.5","rating":4.0,"description":"BoneAge-Assess is a SYNTHETIC/DEMO medical imaging model for pediatric bone-age estimation. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Hand radiograph","outputs":"Bone age + CI","connected_ids":["bf-036-chestx-multi","bf-039-strokect-triage","bf-037-retinascan-dr"]},{"id":"bf-039-strokect-triage","name":"StrokeCT-Triage","domain":"Medical Imaging","modality":"Imaging","org":"DeepCell AI","task":"Acute stroke large-vessel occlusion triage","metric_name":"AUC","metric_value":0.951,"downloads":136806,"license":"BSD-3-Clause","version":"3.4.2","rating":4.8,"description":"StrokeCT-Triage is a SYNTHETIC/DEMO medical imaging model for acute stroke large-vessel occlusion triage. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Head CTA","outputs":"LVO probability + laterality","connected_ids":["bf-036-chestx-multi","bf-038-boneage-assess","bf-037-retinascan-dr"]},{"id":"bf-040-organseg-abdo","name":"OrganSeg-Abdo","domain":"Medical Imaging","modality":"Imaging","org":"NeuroScan Labs","task":"Multi-organ abdominal CT segmentation","metric_name":"Dice","metric_value":0.945,"downloads":220953,"license":"BioFoundry Research","version":"2.7.7","rating":4.6,"description":"OrganSeg-Abdo is a SYNTHETIC/DEMO medical imaging model for multi-organ abdominal ct segmentation. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified imaging data.","inputs":"Abdominal CT volume","outputs":"Per-organ masks","connected_ids":["bf-037-retinascan-dr","bf-038-boneage-assess","bf-039-strokect-triage"]},{"id":"bf-041-readmitrisk-30","name":"ReadmitRisk-30","domain":"Clinical","modality":"Tabular","org":"Insilico BioFoundry","task":"30-day hospital readmission risk","metric_name":"AUROC","metric_value":0.939,"downloads":375745,"license":"MIT","version":"4.3.5","rating":3.8,"description":"ReadmitRisk-30 is a SYNTHETIC/DEMO clinical model for 30-day hospital readmission risk. Outputs are deterministic demo values — NOT for clinical use. 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Trained (synthetically) on de-identified tabular data.","inputs":"ICU time-series","outputs":"Mortality risk","connected_ids":["bf-043-trialmatch-nlp","bf-041-readmitrisk-30","bf-046-phenoextract"]},{"id":"bf-045-druginteract-check","name":"DrugInteract-Check","domain":"Clinical","modality":"Tabular","org":"EMBL-Synthetic","task":"Drug-drug interaction severity","metric_name":"F1","metric_value":0.843,"downloads":86451,"license":"Apache-2.0","version":"2.6.5","rating":4.0,"description":"DrugInteract-Check is a SYNTHETIC/DEMO clinical model for drug-drug interaction severity. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified tabular data.","inputs":"Medication list","outputs":"Interaction severity flags","connected_ids":["bf-044-mortalityicu","bf-041-readmitrisk-30","bf-046-phenoextract"]},{"id":"bf-046-phenoextract","name":"PhenoExtract","domain":"Clinical","modality":"Sequence","org":"BroadOpen Collective","task":"Phenotype extraction from records","metric_name":"F1","metric_value":0.919,"downloads":80990,"license":"Apache-2.0","version":"3.8.7","rating":3.8,"description":"PhenoExtract is a SYNTHETIC/DEMO clinical model for phenotype extraction from records. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified sequence data.","inputs":"Clinical text","outputs":"HPO phenotype terms","connected_ids":["bf-041-readmitrisk-30","bf-045-druginteract-check","bf-043-trialmatch-nlp"]}]