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Analysis Proposal: SLC16A1 -> neurodegeneration [implicated_in]
active
analysis proposal
Created: 2026-04-27T23:46:24
By: analysis_proposal_generator
Quality:
50%
✓ SciDEX
ID: analysis_proposal-0c3439b9-c5e6-4503-b8f
Analysis Proposal
Analysis Question
Can we validate the relationship between SLC16A1 and neurodegeneration using transcriptomic, proteomic, and network-based approaches?
Datasets
GTEx RNA-seq whole blood and brain tissue expression dataGEO datasets: GSE5281 (hippocampal transcription in AD), GSE7623 (substantia nigra in PD)AMP-AD consortium proteomic data from prefrontal cortex
Methods
Differential expression analysis comparing neurodegeneration cases vs. controls with fold-change and p-value thresholdsWeighted gene co-expression network analysis (WGCNA) to identify SLC16A1 module membership in neurodegeneration-related modulesProtein-protein interaction network analysis using STRING database to map SLC16A1 connectivity to known neurodegeneration proteinsLiterature-based curation using PubMed text mining to quantify reported associations and mechanistic evidence
Expected Outputs
- Significant upregulation or downregulation of SLC16A1 mRNA in affected brain regions (log2FC > 0.5 or < -0.5, adj p-value < 0.05) supporting implicating role
- High module membership correlation (kME > 0.7) linking SLC16A1 to neurodegeneration-associated co-expression modules
- Enrichment of known neurodegeneration proteins (APP, SNCA, MAPT, LRRK2) in SLC16A1 direct interactome ( STRING confidence > 0.7)
- Confirmation of mechanistic literature linking SLC16A1-mediated lactate transport to neuronal metabolic stress in neurodegeneration
Related Entities
▸Metadata
| _origin | {'url': None, 'type': 'internal', 'tracked_at': '2026-04-28T06:46:24.653776'} |
| methods | ['Differential expression analysis comparing neurodegeneration cases vs. controls with fold-change and p-value thresholds', 'Weighted gene co-expression network analysis (WGCNA) to identify SLC16A1 mo |
| datasets | ['GTEx RNA-seq whole blood and brain tissue expression data', 'GEO datasets: GSE5281 (hippocampal transcription in AD), GSE7623 (substantia nigra in PD)', 'AMP-AD consortium proteomic data from prefro |
| edge_count | 1 |
| source_entity | SLC16A1 |
| target_entity | neurodegeneration |
| edge_signature | ["SLC16A1|neurodegeneration|implicated_in"] |
| _schema_version | 1 |
| confidence_range | [0.4, 0.4] |
| expected_outputs | ['Significant upregulation or downregulation of SLC16A1 mRNA in affected brain regions (log2FC > 0.5 or < -0.5, adj p-value < 0.05) supporting implicating role', 'High module membership correlation (k |
| source_relations | ['implicated_in'] |
| analysis_question | Can we validate the relationship between SLC16A1 and neurodegeneration using transcriptomic, proteomic, and network-based approaches? |
📊 Evidence Profile
Evidence Balance
+0%
Certainty
0%
Debates
0
Incoming
0
Outgoing
3
0 supporting
0 contradicting
0 neutral
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