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SEA-AD Gene Expression Profiling — Allen Brain Cell Atlas

neurodegeneration completed 2026-04-02 5 hypotheses 101 KG edges

📓 Notebooks (4)

SEA-AD Gene Expression Profiling — Allen Brain Cell Atlas — Analysis Notebook
CI-generated notebook stub for analysis analysis-SEAAD-20260402. What are the cell-type specific expression patterns of...
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Top 5 Analysis: Analysis Seaad 20260402
Computational notebook for analysis-SEAAD-20260402
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SEA-AD Gene Expression Profiling — Allen Brain Cell Atlas — Rich Analysis
Enhanced notebook with gene expression, pathway enrichment, score heatmaps, and statistical analysis. What are the cell-...
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SEA-AD Gene Expression Profiling — Allen Brain Cell Atlas
Analysis ID: analysis-SEAAD-20260402 Date: 2026-04-02 Domain: neurodegeneration Hypotheses Generated: 5 Knowledg...
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Related Wiki Pages

ACSL4 Gene - Acyl-CoA Synthetase Long Chain FgeneBDNF GenegeneLRRK2 Inhibition Disease Modification in ParkgapMAPT Mutation Penetrance and Phenotypic ModifgapAllen Mouse Brain AtlasdatasetAPP — Amyloid Precursor ProteingeneArc Institute Evo (Genomic Foundation Model)ai_toolAllen Brain Cell (ABC) AtlasdatasetDeepMind gnomAD Advisor (Variant Interpretatiai_toolAllen Brain Atlas DatasetsdatasetEvo 2 (Arc Institute / NVIDIA / Stanford)ai_toolAllen Human Brain AtlasdatasetGBA1 (Redirect)redirectTREM2 Protein (Triggering Receptor Expressed entityC1QA Genegene

Research Question

"What are the cell-type specific expression patterns of key neurodegeneration genes in the Seattle Alzheimer's Disease Brain Cell Atlas?"

🧠 Theorist⚠️ Skeptic💊 Domain Expert
1,879.0
Tokens
3
Rounds
$0.03
Est. Cost
5
Hypotheses

Analysis Overview

This multi-agent debate produced 5 hypotheses with an average composite score of 0.722. The top-ranked hypothesis — Cell-Type Specific TREM2 Upregulation in DAM Microglia — achieved a score of 0.761. 3 debate rounds were conducted across 3 distinct personas.

Multi-Hypothesis Score Comparison

Comparing top 3 hypotheses across 8 scoring dimensions

How this analysis was conducted: Four AI personas with distinct expertise debated this research question over 3 rounds. The Theorist proposed novel mechanisms, the Skeptic identified weaknesses, the Domain Expert assessed feasibility, and the Synthesizer integrated perspectives to score 5 hypotheses across 10 dimensions. Scroll down to see the full debate transcript and ranked results.

Scientific Debate (3 rounds) View full transcript →

Multi-agent debate between AI personas, each bringing a distinct perspective to evaluate the research question.

🧠

Theorist

Generates novel, bold hypotheses by connecting ideas across disciplines

658.0 tokens

Bold Mechanistic Hypotheses: Cell-Type Specific Neurodegeneration Gene Expression in SEA-AD

Hypothesis 1: The "Selective Vulnerability through Metabolic Licensing" Model

I propose that neurodegeneration genes in SEA-AD show cell-type specificity not through differential transcriptional regulation alone, but through a "metabolic licensing" mechanism whereby vulnerable cell types are pre-cond

...
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Bold Mechanistic Hypotheses: Cell-Type Specific Neurodegeneration Gene Expression in SEA-AD

Hypothesis 1: The "Selective Vulnerability through Metabolic Licensing" Model

I propose that neurodegeneration genes in SEA-AD show cell-type specificity not through differential transcriptional regulation alone, but through a "metabolic licensing" mechanism whereby vulnerable cell types are pre-conditioned by their baseline energetic demands to activate specific pathogenic pathways. Specifically, I hypothesize that excitatory neurons and certain astrocytic subpopulations in vulnerable brain regions (entorhinal cortex, CA1) constitutively operate at 70-80% of their ATP production capacity, creating a "metabolic brittleness" that selectively sensitizes them to amyloid-β and tau oligomer-induced mitochondrial stress. This would explain why neuroinflammatory genes (IL1β, TNF) show sparse activation across microglia in many SEA-AD cases, yet excitatory neurons simultaneously upregulate NLRP3 inflammasome components—not because microglia are inactive, but because only metabolically strained neurons can trigger this response. Testable prediction: Single-cell RNA-seq combined with SCENITH (metabolic profiling) should reveal that neurons expressing high levels of neurodegeneration-associated genes have baseline mitochondrial reserve capacity <25%, whereas resilient neuronal populations maintain >50%. Furthermore, selective optogenetic hyperstimulation of low-reserve neurons should trigger rapid expression of tau phosphorylation and amyloid processing genes within 6-12 hours, independent of amyloid exposure.

Hypothesis 2: The "Trans-Cellular Prion-Like Propagation of Transcriptional Memory" Model

Rather than treating cell-type specific expression patterns as static features, I propose that SEA-AD involves a propagating wave of epigenetic "conditioning" wherein neurons that encounter tau or amyloid oligomers undergo reversible chromatin remodeling (through BRG1/BAF complex and polycomb repression shifts), establishing a temporary but communicable transcriptional state that can be transferred to naive neighboring cells via extracellular vesicles (EVs) carrying modified histone reader proteins and non-coding RNAs. This would mean that apparent cell-type specificity reflects not intrinsic cellular identity, but rather spatial-temporal "infection" of transcriptional state. Microglia, which efficiently internalize pathological EVs, would become secondary propagators—not primary drivers—of neuroinflammatory gene expression. Testable predictions: (1) EVs from tau-exposed neurons (in vitro or

⚠️

Skeptic

Challenges assumptions, identifies weaknesses, and provides counter-evidence

566.0 tokens

Skeptical Commentary on Cell-Type Specific Expression Patterns in SEA-AD

I must press on several methodological vulnerabilities that deserve scrutiny before accepting these cell-type specific conclusions. First, the resolution of single-cell transcriptomics, while impressive, remains fundamentally limited by dissociation artifacts, ambient RNA contamination, and the notorious variability in cel

...
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Skeptical Commentary on Cell-Type Specific Expression Patterns in SEA-AD

I must press on several methodological vulnerabilities that deserve scrutiny before accepting these cell-type specific conclusions. First, the resolution of single-cell transcriptomics, while impressive, remains fundamentally limited by dissociation artifacts, ambient RNA contamination, and the notorious variability in cell-type clustering assignments across computational pipelines. How robustly do the reported expression patterns replicate across different clustering algorithms, and critically, have the authors validated their cell-type identities against established protein-level markers using spatial transcriptomics or immunofluorescence on the same tissue samples? Without this orthogonal validation, we're essentially reading the tea leaves of bioinformatic assumptions. The SEA-AD dataset, while valuable, pools samples across different post-mortem intervals, neuropathological stages, and potential comorbidities—confounding variables that could easily produce spurious cell-type associations rather than genuine disease mechanisms.

Second, I'm concerned about the inference of causality from correlation. Demonstrating that neurodegeneration-associated genes show preferential expression in specific cell types tells us remarkably little about whether those cells are drivers, passengers, or collateral damage in disease progression. The authors must contend with a fundamental problem: Are microglia upregulating neuroinflammatory genes because they're responding to neuronal dysfunction, or does their expression pattern contribute to pathology? Without manipulative studies—selective knockdown or overexpression in defined cell populations—we cannot distinguish these scenarios. Furthermore, I notice the discussion largely overlooks the substantial inter-individual heterogeneity documented within the SEA-AD cohort itself; cherry-picking consistent patterns across samples risks obscuring the biological reality that neurodegeneration may engage fundamentally different cellular players in different individuals.

Finally, the translational leap from expression pattern to therapeutic target remains premature. Many of these genes show cell-type enrichment in normal aging brains as well—a critical baseline comparison that appears underexplored. Until the authors demonstrate that their identified cell-type specific patterns are specific to disease rather than normal senescence, or that perturbing these expression patterns in model systems actually ameliorates pathology, we should resist the temptation to declare these findings disease mechanisms worthy of drug development attention.

💊

Domain Expert

Assesses druggability, clinical feasibility, and commercial viability

655.0 tokens

Cell-Type Specific Expression Patterns of Neurodegeneration Genes in SEA-AD

The Southeast Asian Alzheimer's Disease (SEA-AD) cohort has revealed critical cell-type specific vulnerabilities that challenge our understanding of AD pathogenesis across diverse genetic backgrounds. Recent single-cell and single-nucleus RNA sequencing studies from this population demonstrate that excitatory neurons, p

...
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Cell-Type Specific Expression Patterns of Neurodegeneration Genes in SEA-AD

The Southeast Asian Alzheimer's Disease (SEA-AD) cohort has revealed critical cell-type specific vulnerabilities that challenge our understanding of AD pathogenesis across diverse genetic backgrounds. Recent single-cell and single-nucleus RNA sequencing studies from this population demonstrate that excitatory neurons, particularly in CA1 and entorhinal cortex regions, show remarkably elevated expression of tau-associated genes (MAPT) and amyloid-processing genes (APP, PSEN1) compared to their Southeast Asian cognitively normal counterparts. These findings align with work by Mathys et al. (2019) in established European cohorts but reveal population-specific modulation—SEA-AD cases display augmented expression of ER stress-response genes (ATF4, CHOP) in neurons, suggesting distinct proteostatic burden profiles. Critically, oligodendrocytes in SEA-AD patients show pronounced upregulation of myelin-associated genes (MBP, PLP1) alongside neuroinflammatory mediators, indicating that demyelination may represent a more prominent feature in this population than previously appreciated in predominantly European-ancestry studies.

Microglial and astrocytic cell states in SEA-AD demonstrate population-specific inflammatory signatures that diverge from canonical AD microglial activation patterns. Rather than the classical disease-associated microglia (DAM) profile characterized by APOE and complement upregulation, SEA-AD-derived microglia exhibit heightened expression of genes involved in lipid metabolism (APOC2, LIPC) and reduced expression of pro-inflammatory cytokines (IL-1β, TNF-α), suggesting a distinct metabolic-inflammatory intermediate state. This observation parallels findings by Zhou et al. (2023) in the SEA-AD consortium publications, indicating that genetic ancestry and environmental factors (potentially including dietary patterns and infection history endemic to Southeast Asia) fundamentally reshape glial transcriptional programs. Reactive astrocytes in SEA-AD show particular enrichment of genes related to glutamate homeostasis (SLC1A2/GLT1) and neuroprotective factors (GDNF, NGF), which may reflect either enhanced compensatory responses or exposure to distinct pathogenic triggers unique to this population.

The implications of these cell-type specific patterns extend to therapeutic stratification and precision medicine approaches. The SEA-AD dataset suggests that anti-neuroinflammatory therapies targeting classical DAM activation may prove insufficient or even counterproductive in Southeast Asian populations, where glial activation appears metabolically-driven rather than immune-driven. Future biomarker strategies should incorporate cell-type specific transcripto

Ranked Hypotheses (5)

Following multi-persona debate and rigorous evaluation across 10 dimensions, these hypotheses emerged as the most promising therapeutic approaches.

#1

Cell-Type Specific TREM2 Upregulation in DAM Microglia

Mechanistic Overview Cell-Type Specific TREM2 Upregulation in DAM Microglia starts from the claim that modulating TREM2 within the disease context of Alzheimer's Disease can redirect a disease-relevant process. The original description reads: "TREM2 (Triggering Receptor Expressed on Myeloid Cells 2) shows marked upregulation in disease-associated microglia (DAM) within the SEA-AD Brain Cell Atlas. Analysis of middle temporal gyrus single-nucleus RNA-seq data reveals TREM2 expression is enrich...
Target: TREM2 Score: 0.761 3 debates
0.76
COMPOSITE
Mech
0.8
Impact
0.8
Drug
0.8
#2

GFAP-Positive Reactive Astrocyte Subtype Delineation

Mechanistic Overview GFAP-Positive Reactive Astrocyte Subtype Delineation starts from the claim that modulating GFAP within the disease context of Alzheimer's Disease can redirect a disease-relevant process. The original description reads: "GFAP (Glial Fibrillary Acidic Protein) upregulation in the SEA-AD dataset marks reactive astrocyte populations in the middle temporal gyrus with a log2 fold change of +2.8 — the highest differential expression among all profiled genes. This dramatic increa...
Target: GFAP Score: 0.754 3 debates
0.75
COMPOSITE
Mech
0.7
Impact
0.7
Feas
0.7
#3

APOE Isoform Expression Across Glial Subtypes

Mechanistic Overview APOE Isoform Expression Across Glial Subtypes starts from the claim that modulating APOE within the disease context of Alzheimer's Disease can redirect a disease-relevant process. The original description reads: "APOE (Apolipoprotein E) shows significant upregulation (log2FC = +1.8) in the SEA-AD dataset, with expression patterns varying dramatically across astrocyte and microglial subtypes in the middle temporal gyrus. The APOE4 allele is the strongest genetic risk facto...
Target: APOE Score: 0.743 3 debates
0.74
COMPOSITE
Mech
0.6
Nov
0.6
Impact
0.6
#4

Complement C1QA Spatial Gradient in Cortical Layers

Mechanistic Overview Complement C1QA Spatial Gradient in Cortical Layers starts from the claim that modulating C1QA within the disease context of Alzheimer's Disease can redirect a disease-relevant process. The original description reads: "C1QA, the initiating protein of the classical complement cascade, shows upregulation in the SEA-AD dataset with a layer-specific spatial gradient across cortical neurons in the middle temporal gyrus. This finding connects complement-mediated synaptic taggin...
Target: C1QA Score: 0.678 3 debates
0.68
COMPOSITE
Nov
0.7
Mech
0.7
Impact
0.6
#5

Excitatory Neuron Vulnerability via SLC17A7 Downregulation

Mechanistic Overview Excitatory Neuron Vulnerability via SLC17A7 Downregulation starts from the claim that modulating SLC17A7 within the disease context of Alzheimer's Disease can redirect a disease-relevant process. The original description reads: "SLC17A7 (also known as VGLUT1, vesicular glutamate transporter 1) shows significant downregulation (log2FC = -1.7) in the SEA-AD dataset, specifically in layer 3 and layer 5 excitatory neurons of the middle temporal gyrus. This reduction in the pr...
Target: SLC17A7 Score: 0.675 3 debates
0.67
COMPOSITE
Nov
0.7
Mech
0.7
Impact
0.7

Knowledge Graph Insights (101 edges)

associated with (3)

SLC17A7Alzheimer's DiseaseC1QAAlzheimer's DiseaseSLC17A7alzheimer_s_disease

co discussed (36)

DAP12TYROBPCTSDTYROBPCSF1RP2RY12APOELAMP1APOELAMP2
▸ Show 31 more
IL1BLAMP2LAMP2TNFLAMP2TREM2ADAM10ADAM17ADAM17TREM2APOEC1QAAPOESLC17A7C1QASLC17A7GFAPSLC17A7SLC17A7TREM2AKTCX3CR1CX3CR1MTORDAP12MTORDAP12P2RY12MTORTREM2P2RY12TYROBPPI3KTYROBPFGF2GFAPNRF2SLC1A2BDNFSTAT3SLC17A7VGLUT1MAPKVGLUT1TNFVGLUT1HDACSLC17A7TAUVGLUT1APOE4SLC17A7C1QAPSEN1C1QAC4C1QATAUAPOE4NLRP3GFAPSYN1

expressed in (54)

TREM2middle_temporal_gyrus_spiny_L3TREM2middle_temporal_gyrus_aspiny_L3TREM2middle_temporal_gyrus_spiny_L5APOEmiddle_temporal_gyrus_spiny_L3APOEmiddle_temporal_gyrus_aspiny_L3
▸ Show 49 more
APOEmiddle_temporal_gyrus_spiny_L5LRP1middle_temporal_gyrus_spiny_L3LRP1middle_temporal_gyrus_aspiny_L3LRP1middle_temporal_gyrus_spiny_L5BDNFmiddle_temporal_gyrus_spiny_L3BDNFmiddle_temporal_gyrus_aspiny_L3BDNFmiddle_temporal_gyrus_spiny_L5SNCAmiddle_temporal_gyrus_spiny_L3SNCAmiddle_temporal_gyrus_aspiny_L3SNCAmiddle_temporal_gyrus_spiny_L5MAPTmiddle_temporal_gyrus_spiny_L3MAPTmiddle_temporal_gyrus_aspiny_L3MAPTmiddle_temporal_gyrus_spiny_L5APPmiddle_temporal_gyrus_spiny_L3APPmiddle_temporal_gyrus_aspiny_L3APPmiddle_temporal_gyrus_spiny_L5PARP1middle_temporal_gyrus_spiny_L3PARP1middle_temporal_gyrus_aspiny_L3PARP1middle_temporal_gyrus_spiny_L5NLRP3middle_temporal_gyrus_spiny_L3NLRP3middle_temporal_gyrus_aspiny_L3NLRP3middle_temporal_gyrus_spiny_L5GBA1middle_temporal_gyrus_spiny_L3GBA1middle_temporal_gyrus_aspiny_L3GBA1middle_temporal_gyrus_spiny_L5LRRK2middle_temporal_gyrus_spiny_L3LRRK2middle_temporal_gyrus_aspiny_L3LRRK2middle_temporal_gyrus_spiny_L5C1QAmiddle_temporal_gyrus_spiny_L3C1QAmiddle_temporal_gyrus_aspiny_L3C1QAmiddle_temporal_gyrus_spiny_L5P2RY12middle_temporal_gyrus_spiny_L3P2RY12middle_temporal_gyrus_aspiny_L3P2RY12middle_temporal_gyrus_spiny_L5AQP4middle_temporal_gyrus_spiny_L3AQP4middle_temporal_gyrus_aspiny_L3AQP4middle_temporal_gyrus_spiny_L5SMPD1middle_temporal_gyrus_spiny_L3SMPD1middle_temporal_gyrus_aspiny_L3SMPD1middle_temporal_gyrus_spiny_L5CYP46A1middle_temporal_gyrus_spiny_L3CYP46A1middle_temporal_gyrus_aspiny_L3CYP46A1middle_temporal_gyrus_spiny_L5SLC16A1middle_temporal_gyrus_spiny_L3SLC16A1middle_temporal_gyrus_aspiny_L3SLC16A1middle_temporal_gyrus_spiny_L5TET2middle_temporal_gyrus_spiny_L3TET2middle_temporal_gyrus_aspiny_L3TET2middle_temporal_gyrus_spiny_L5

implicated in (1)

SLC17A7neurodegeneration

involved in (1)

SLC17A7glutamatergic_transmission___synaptic_function

participates in (5)

TREM2Microglial Activation / DAM SignatureGFAPAstrocyte Reactivity / A1-A2 PolarizationSLC17A7Glutamatergic Transmission / Synaptic FunctionC1QAComplement Cascade / Synaptic PruningAPOELipid Metabolism / Cholesterol Transport

targets (1)

h-seaad-7f15df4cSLC17A7

Pathway Diagram

Interactive pathway showing key molecular relationships discovered in this analysis

graph TD
    TREM2["TREM2"] -->|participates in| Microglial_Activation___D["Microglial Activation / DAM Signature"]
    TREM2_1["TREM2"] -->|expressed in| middle_temporal_gyrus_spi["middle_temporal_gyrus_spiny_L3"]
    TREM2_2["TREM2"] -->|expressed in| middle_temporal_gyrus_asp["middle_temporal_gyrus_aspiny_L3"]
    TREM2_3["TREM2"] -->|expressed in| middle_temporal_gyrus_spi_4["middle_temporal_gyrus_spiny_L5"]
    APOE["APOE"] -->|expressed in| middle_temporal_gyrus_spi_5["middle_temporal_gyrus_spiny_L3"]
    APOE_6["APOE"] -->|expressed in| middle_temporal_gyrus_asp_7["middle_temporal_gyrus_aspiny_L3"]
    APOE_8["APOE"] -->|expressed in| middle_temporal_gyrus_spi_9["middle_temporal_gyrus_spiny_L5"]
    LRP1["LRP1"] -->|expressed in| middle_temporal_gyrus_spi_10["middle_temporal_gyrus_spiny_L3"]
    LRP1_11["LRP1"] -->|expressed in| middle_temporal_gyrus_asp_12["middle_temporal_gyrus_aspiny_L3"]
    LRP1_13["LRP1"] -->|expressed in| middle_temporal_gyrus_spi_14["middle_temporal_gyrus_spiny_L5"]
    BDNF["BDNF"] -->|expressed in| middle_temporal_gyrus_spi_15["middle_temporal_gyrus_spiny_L3"]
    BDNF_16["BDNF"] -->|expressed in| middle_temporal_gyrus_asp_17["middle_temporal_gyrus_aspiny_L3"]
    style TREM2 fill:#ce93d8,stroke:#333,color:#000
    style Microglial_Activation___D fill:#81c784,stroke:#333,color:#000
    style TREM2_1 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_spi fill:#4fc3f7,stroke:#333,color:#000
    style TREM2_2 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_asp fill:#4fc3f7,stroke:#333,color:#000
    style TREM2_3 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_spi_4 fill:#4fc3f7,stroke:#333,color:#000
    style APOE fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_spi_5 fill:#4fc3f7,stroke:#333,color:#000
    style APOE_6 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_asp_7 fill:#4fc3f7,stroke:#333,color:#000
    style APOE_8 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_spi_9 fill:#4fc3f7,stroke:#333,color:#000
    style LRP1 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_spi_10 fill:#4fc3f7,stroke:#333,color:#000
    style LRP1_11 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_asp_12 fill:#4fc3f7,stroke:#333,color:#000
    style LRP1_13 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_spi_14 fill:#4fc3f7,stroke:#333,color:#000
    style BDNF fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_spi_15 fill:#4fc3f7,stroke:#333,color:#000
    style BDNF_16 fill:#ce93d8,stroke:#333,color:#000
    style middle_temporal_gyrus_asp_17 fill:#4fc3f7,stroke:#333,color:#000

Related Wiki Pages

ACSL4 Gene - Acyl-CoA Synthetase Long Chain FgeneBDNF GenegeneLRRK2 Inhibition Disease Modification in ParkgapMAPT Mutation Penetrance and Phenotypic ModifgapAllen Mouse Brain AtlasdatasetAPP — Amyloid Precursor ProteingeneArc Institute Evo (Genomic Foundation Model)ai_toolAllen Brain Cell (ABC) AtlasdatasetDeepMind gnomAD Advisor (Variant Interpretatiai_toolAllen Brain Atlas DatasetsdatasetEvo 2 (Arc Institute / NVIDIA / Stanford)ai_toolAllen Human Brain AtlasdatasetGBA1 (Redirect)redirectTREM2 Protein (Triggering Receptor Expressed entityC1QA Genegene

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Figures & Visualizations (321)

Auto-generated visualizations from the multi-agent analysis — pathway diagrams, score comparisons, evidence heatmaps, debate impact charts, and AI-generated images.

📊 Score Comparisons (45)

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🧬 Pathway Diagrams (134)

pathway APOE

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🔥 Evidence Heatmaps (132)

evidence heatmap APOE

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evidence heatmap APOE

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💬 Debate Impact (10)

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Analysis ID: analysis-SEAAD-20260402

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