🧠
Neurodegeneration Risk Predictor
active
model
Created: 2026-04-04T05:03:31
By: test
Quality:
90%
✓ SciDEX
ID: model-29ce54ef-040c-4831-97b6-4850faa315
🧮 Model Details
Deep Learning
Internalcandidate
ARCHITECTURE
pytorch
METRICS
parameter_count
1500
training_config
{'epochs': 100, 'batch_size': 32}
benchmark_name
OT-AD Target Ranking v1
evaluation_metrics
{'f1': 0.84, 'accuracy': 0.87}
EVALUATION CONTEXT
Dataset:SEA-AD Snyder et al. 2022 (Allen Institute) — donor-level neurodegeneration severity labels
Benchmark:OT-AD Target Ranking v1
Training data:SEA-AD bulk RNA-seq + neuropathology scores (535 donors, 84 brain regions)
Related Entities
▸Metadata
| _origin | {'url': None, 'type': 'internal', 'tracked_at': '2026-04-04T05:03:31.017793'} |
| framework | pytorch |
| description | Multi-layer neural network predicting neurodegeneration risk from gene expression, neuropathology, and clinical features. Trained on the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) dataset. |
| benchmark_id | bench_ot_ad_target_ranking_v1 |
| model_family | deep_learning |
| training_data | SEA-AD bulk RNA-seq + neuropathology scores (535 donors, 84 brain regions) |
| benchmark_name | OT-AD Target Ranking v1 |
| _schema_version | 1 |
| parameter_count | 1500 |
| training_config | {'epochs': 100, 'batch_size': 32} |
| evaluation_dataset | SEA-AD Snyder et al. 2022 (Allen Institute) — donor-level neurodegeneration severity labels |
| evaluation_metrics | {'f1': 0.84, 'accuracy': 0.87} |
📊 Evidence Profile
Evidence Balance
+0%
Certainty
25%
Debates
0
Incoming
5
Outgoing
13
0 supporting
0 contradicting
0 neutral
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