What amyloid threshold level is required for optimal clinical benefit in early AD?¶
Notebook ID: nb-SDA-2026-04-16-gap-pubmed-20260410-192526-f2bbb9ab · Analysis: SDA-2026-04-16-gap-pubmed-20260410-192526-f2bbb9ab · Generated: 2026-04-21T18:45:51
Research question¶
While the study demonstrates dose-response relationships between amyloid levels and outcomes, it doesn't establish specific threshold values for clinical benefit. Defining these thresholds is critical for treatment optimization and stopping rules in clinical practice.
Gap type: open_question Source paper: Posttreatment Amyloid Levels and Clinical Outcomes Following Donanemab for Early Symptomatic Alzheimer Disease: A Secondary Analysis of the TRAILBLAZER-ALZ 2 Randomized Clinical Trial. (2025, JAMA neurology, PMID:41082199)
Approach¶
This notebook is generated programmatically from real Forge tool calls and SciDEX debate data. Forge tools used: PubMed Search, MyGene, STRING PPI, Reactome pathways, Enrichr.
Debate Summary¶
Quality score: 0.76 · Rounds: 4
1. Target gene annotations (MyGene)¶
In [1]:
import pandas as pd
ann_rows = [{'gene': 'TREM2', 'name': 'triggering receptor expressed on myeloid cells 2', 'summary': 'This gene encodes a membrane protein that forms a receptor signaling complex with the TYRO protein tyrosine kinase bindi'}]
pd.DataFrame(ann_rows)
| gene | name | summary | |
|---|---|---|---|
| 0 | TREM2 | triggering receptor expressed on myeloid cells 2 | This gene encodes a membrane protein that form... |
2. GO Biological Process enrichment (Enrichr)¶
In [2]:
go_bp = [{'rank': 1, 'term': 'Regulation Of Interleukin-6 Production (GO:0032675)', 'p_value': 1.0048399935913338e-12, 'odds_ratio': 162.55373831775702, 'genes': ['SYK', 'IL1B', 'SIRPA', 'PTPN11', 'TREM2', 'PTPN6', 'CD47']}, {'rank': 2, 'term': 'Regulation Of Phagocytosis (GO:0050764)', 'p_value': 5.048610594157601e-10, 'odds_ratio': 188.03773584905662, 'genes': ['SYK', 'IL1B', 'SIRPA', 'TREM2', 'CD47']}, {'rank': 3, 'term': 'Regulation Of ERK1 And ERK2 Cascade (GO:0070372)', 'p_value': 1.415343656859335e-08, 'odds_ratio': 55.31372549019608, 'genes': ['SYK', 'IL1B', 'SIRPA', 'PTPN11', 'TREM2', 'PTPN6']}, {'rank': 4, 'term': 'Synapse Pruning (GO:0098883)', 'p_value': 1.9067019051840425e-08, 'odds_ratio': 999.0, 'genes': ['C1QB', 'TREM2', 'C1QC']}, {'rank': 5, 'term': 'Regulation Of Tumor Necrosis Factor Production (GO:0032680)', 'p_value': 2.7217119623639505e-08, 'odds_ratio': 81.40573770491804, 'genes': ['SYK', 'SIRPA', 'PTPN11', 'PTPN6', 'CD47']}, {'rank': 6, 'term': 'Cellular Response To Cytokine Stimulus (GO:0071345)', 'p_value': 5.6575802607493816e-08, 'odds_ratio': 43.450331125827816, 'genes': ['IL1B', 'SIRPA', 'PTPN11', 'PTPN6', 'CD47', 'SIRT1']}, {'rank': 7, 'term': 'Positive Regulation Of Phagocytosis (GO:0050766)', 'p_value': 9.742769899857603e-08, 'odds_ratio': 129.4090909090909, 'genes': ['IL1B', 'SIRPA', 'TREM2', 'CD47']}, {'rank': 8, 'term': 'Positive Regulation Of Endocytosis (GO:0045807)', 'p_value': 2.9769516535686854e-07, 'odds_ratio': 96.53333333333333, 'genes': ['IL1B', 'SIRPA', 'TREM2', 'CD47']}, {'rank': 9, 'term': 'Cytokine-Mediated Signaling Pathway (GO:0019221)', 'p_value': 9.108569513553911e-07, 'odds_ratio': 39.15277777777778, 'genes': ['SYK', 'IL1B', 'PTPN11', 'PTPN6', 'SIRT1']}, {'rank': 10, 'term': 'Negative Regulation Of Inflammatory Response (GO:0050728)', 'p_value': 1.1700096460875274e-06, 'odds_ratio': 67.55480033984706, 'genes': ['SYK', 'SIRPA', 'TREM2', 'PTPN6']}]
go_df = pd.DataFrame(go_bp)[['term','p_value','odds_ratio','genes']]
go_df['p_value'] = go_df['p_value'].apply(lambda p: f'{p:.2e}')
go_df['odds_ratio'] = go_df['odds_ratio'].round(1)
go_df['term'] = go_df['term'].str[:60]
go_df['n_hits'] = go_df['genes'].apply(len)
go_df['genes'] = go_df['genes'].apply(lambda g: ', '.join(g))
go_df[['term','n_hits','p_value','odds_ratio','genes']]
| term | n_hits | p_value | odds_ratio | genes | |
|---|---|---|---|---|---|
| 0 | Regulation Of Interleukin-6 Production (GO:003... | 7 | 1.00e-12 | 162.6 | SYK, IL1B, SIRPA, PTPN11, TREM2, PTPN6, CD47 |
| 1 | Regulation Of Phagocytosis (GO:0050764) | 5 | 5.05e-10 | 188.0 | SYK, IL1B, SIRPA, TREM2, CD47 |
| 2 | Regulation Of ERK1 And ERK2 Cascade (GO:0070372) | 6 | 1.42e-08 | 55.3 | SYK, IL1B, SIRPA, PTPN11, TREM2, PTPN6 |
| 3 | Synapse Pruning (GO:0098883) | 3 | 1.91e-08 | 999.0 | C1QB, TREM2, C1QC |
| 4 | Regulation Of Tumor Necrosis Factor Production... | 5 | 2.72e-08 | 81.4 | SYK, SIRPA, PTPN11, PTPN6, CD47 |
| 5 | Cellular Response To Cytokine Stimulus (GO:007... | 6 | 5.66e-08 | 43.5 | IL1B, SIRPA, PTPN11, PTPN6, CD47, SIRT1 |
| 6 | Positive Regulation Of Phagocytosis (GO:0050766) | 4 | 9.74e-08 | 129.4 | IL1B, SIRPA, TREM2, CD47 |
| 7 | Positive Regulation Of Endocytosis (GO:0045807) | 4 | 2.98e-07 | 96.5 | IL1B, SIRPA, TREM2, CD47 |
| 8 | Cytokine-Mediated Signaling Pathway (GO:0019221) | 5 | 9.11e-07 | 39.2 | SYK, IL1B, PTPN11, PTPN6, SIRT1 |
| 9 | Negative Regulation Of Inflammatory Response (... | 4 | 1.17e-06 | 67.6 | SYK, SIRPA, TREM2, PTPN6 |
In [3]:
import matplotlib.pyplot as plt
import numpy as np
go_bp = [{'rank': 1, 'term': 'Regulation Of Interleukin-6 Production (GO:0032675)', 'p_value': 1.0048399935913338e-12, 'odds_ratio': 162.55373831775702, 'genes': ['SYK', 'IL1B', 'SIRPA', 'PTPN11', 'TREM2', 'PTPN6', 'CD47']}, {'rank': 2, 'term': 'Regulation Of Phagocytosis (GO:0050764)', 'p_value': 5.048610594157601e-10, 'odds_ratio': 188.03773584905662, 'genes': ['SYK', 'IL1B', 'SIRPA', 'TREM2', 'CD47']}, {'rank': 3, 'term': 'Regulation Of ERK1 And ERK2 Cascade (GO:0070372)', 'p_value': 1.415343656859335e-08, 'odds_ratio': 55.31372549019608, 'genes': ['SYK', 'IL1B', 'SIRPA', 'PTPN11', 'TREM2', 'PTPN6']}, {'rank': 4, 'term': 'Synapse Pruning (GO:0098883)', 'p_value': 1.9067019051840425e-08, 'odds_ratio': 999.0, 'genes': ['C1QB', 'TREM2', 'C1QC']}, {'rank': 5, 'term': 'Regulation Of Tumor Necrosis Factor Production (GO:0032680)', 'p_value': 2.7217119623639505e-08, 'odds_ratio': 81.40573770491804, 'genes': ['SYK', 'SIRPA', 'PTPN11', 'PTPN6', 'CD47']}, {'rank': 6, 'term': 'Cellular Response To Cytokine Stimulus (GO:0071345)', 'p_value': 5.6575802607493816e-08, 'odds_ratio': 43.450331125827816, 'genes': ['IL1B', 'SIRPA', 'PTPN11', 'PTPN6', 'CD47', 'SIRT1']}, {'rank': 7, 'term': 'Positive Regulation Of Phagocytosis (GO:0050766)', 'p_value': 9.742769899857603e-08, 'odds_ratio': 129.4090909090909, 'genes': ['IL1B', 'SIRPA', 'TREM2', 'CD47']}, {'rank': 8, 'term': 'Positive Regulation Of Endocytosis (GO:0045807)', 'p_value': 2.9769516535686854e-07, 'odds_ratio': 96.53333333333333, 'genes': ['IL1B', 'SIRPA', 'TREM2', 'CD47']}]
terms = [t['term'][:45] for t in go_bp][::-1]
neglogp = [-np.log10(max(t['p_value'], 1e-300)) for t in go_bp][::-1]
fig, ax = plt.subplots(figsize=(9, 4.5))
ax.barh(terms, neglogp, color='#4fc3f7')
ax.set_xlabel('-log10(p-value)')
ax.set_title('Top GO:BP enrichment (Enrichr)')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()
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3. STRING protein interaction network¶
In [4]:
ppi = [{'protein1': 'CRP', 'protein2': 'FCGR1A', 'score': 0.434, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.434}, {'protein1': 'CRP', 'protein2': 'C1QC', 'score': 0.499, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'CRP', 'protein2': 'C1QA', 'score': 0.63, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'CRP', 'protein2': 'C1QB', 'score': 0.689, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.405, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'C1QB', 'protein2': 'C1QA', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.967, 'dscore': 0.9, 'tscore': 0.981}, {'protein1': 'C1QB', 'protein2': 'C1QC', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.92, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'CD47', 'protein2': 'PTPN6', 'score': 0.499, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'CD47', 'protein2': 'TREM2', 'score': 0.539, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.539}, {'protein1': 'CD47', 'protein2': 'PTPN11', 'score': 0.674, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0.375}, {'protein1': 'CD47', 'protein2': 'SIRPA', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.939, 'dscore': 0.9, 'tscore': 0.986}, {'protein1': 'FCGR1A', 'protein2': 'SYK', 'score': 0.964, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.4, 'dscore': 0.9, 'tscore': 0.457}, {'protein1': 'TREM2', 'protein2': 'SIRPA', 'score': 0.91, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.141}, {'protein1': 'TREM2', 'protein2': 'SYK', 'score': 0.929, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.323}, {'protein1': 'C1QC', 'protein2': 'C1QA', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.96, 'dscore': 0.9, 'tscore': 0.981}, {'protein1': 'SYK', 'protein2': 'PTPN6', 'score': 0.825, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0.36, 'tscore': 0.538}, {'protein1': 'SYK', 'protein2': 'SIRPA', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'SYK', 'protein2': 'PTPN11', 'score': 0.945, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.479}, {'protein1': 'PTPN6', 'protein2': 'SIRPA', 'score': 0.845, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.618, 'dscore': 0.5, 'tscore': 0.254}, {'protein1': 'PTPN6', 'protein2': 'PTPN11', 'score': 0.947, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.079, 'dscore': 0.9, 'tscore': 0.474}, {'protein1': 'SIRPA', 'protein2': 'PTPN11', 'score': 0.996, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.677, 'dscore': 0.5, 'tscore': 0.982}]
ppi_df = pd.DataFrame(ppi).sort_values('score', ascending=False)
display_cols = [c for c in ['protein1','protein2','score','escore','tscore'] if c in ppi_df.columns]
print(f'{len(ppi_df)} STRING edges')
ppi_df[display_cols].head(20)
20 STRING edges
| protein1 | protein2 | score | escore | tscore | |
|---|---|---|---|---|---|
| 13 | C1QC | C1QA | 0.999 | 0.960 | 0.981 |
| 9 | CD47 | SIRPA | 0.999 | 0.939 | 0.986 |
| 5 | C1QB | C1QC | 0.999 | 0.920 | 0.982 |
| 4 | C1QB | C1QA | 0.999 | 0.967 | 0.981 |
| 19 | SIRPA | PTPN11 | 0.996 | 0.677 | 0.982 |
| 10 | FCGR1A | SYK | 0.964 | 0.400 | 0.457 |
| 18 | PTPN6 | PTPN11 | 0.947 | 0.079 | 0.474 |
| 16 | SYK | PTPN11 | 0.945 | 0.000 | 0.479 |
| 12 | TREM2 | SYK | 0.929 | 0.000 | 0.323 |
| 11 | TREM2 | SIRPA | 0.910 | 0.000 | 0.141 |
| 15 | SYK | SIRPA | 0.900 | 0.000 | 0.000 |
| 17 | PTPN6 | SIRPA | 0.845 | 0.618 | 0.254 |
| 14 | SYK | PTPN6 | 0.825 | 0.457 | 0.538 |
| 3 | CRP | C1QB | 0.689 | 0.405 | 0.000 |
| 8 | CD47 | PTPN11 | 0.674 | 0.000 | 0.375 |
| 2 | CRP | C1QA | 0.630 | 0.292 | 0.000 |
| 7 | CD47 | TREM2 | 0.539 | 0.000 | 0.539 |
| 1 | CRP | C1QC | 0.499 | 0.000 | 0.000 |
| 6 | CD47 | PTPN6 | 0.499 | 0.000 | 0.000 |
| 0 | CRP | FCGR1A | 0.434 | 0.000 | 0.434 |
In [5]:
import math
ppi = [{'protein1': 'CRP', 'protein2': 'FCGR1A', 'score': 0.434, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.434}, {'protein1': 'CRP', 'protein2': 'C1QC', 'score': 0.499, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'CRP', 'protein2': 'C1QA', 'score': 0.63, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'CRP', 'protein2': 'C1QB', 'score': 0.689, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.405, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'C1QB', 'protein2': 'C1QA', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.967, 'dscore': 0.9, 'tscore': 0.981}, {'protein1': 'C1QB', 'protein2': 'C1QC', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.92, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'CD47', 'protein2': 'PTPN6', 'score': 0.499, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'CD47', 'protein2': 'TREM2', 'score': 0.539, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.539}, {'protein1': 'CD47', 'protein2': 'PTPN11', 'score': 0.674, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0.375}, {'protein1': 'CD47', 'protein2': 'SIRPA', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.939, 'dscore': 0.9, 'tscore': 0.986}, {'protein1': 'FCGR1A', 'protein2': 'SYK', 'score': 0.964, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.4, 'dscore': 0.9, 'tscore': 0.457}, {'protein1': 'TREM2', 'protein2': 'SIRPA', 'score': 0.91, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.141}, {'protein1': 'TREM2', 'protein2': 'SYK', 'score': 0.929, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.323}, {'protein1': 'C1QC', 'protein2': 'C1QA', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.96, 'dscore': 0.9, 'tscore': 0.981}, {'protein1': 'SYK', 'protein2': 'PTPN6', 'score': 0.825, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0.36, 'tscore': 0.538}, {'protein1': 'SYK', 'protein2': 'SIRPA', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'SYK', 'protein2': 'PTPN11', 'score': 0.945, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.479}, {'protein1': 'PTPN6', 'protein2': 'SIRPA', 'score': 0.845, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.618, 'dscore': 0.5, 'tscore': 0.254}, {'protein1': 'PTPN6', 'protein2': 'PTPN11', 'score': 0.947, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.079, 'dscore': 0.9, 'tscore': 0.474}, {'protein1': 'SIRPA', 'protein2': 'PTPN11', 'score': 0.996, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.677, 'dscore': 0.5, 'tscore': 0.982}]
if ppi:
nodes = sorted({p for e in ppi for p in (e['protein1'], e['protein2'])})
n = len(nodes)
pos = {n_: (math.cos(2*math.pi*i/n), math.sin(2*math.pi*i/n)) for i, n_ in enumerate(nodes)}
fig, ax = plt.subplots(figsize=(7, 7))
for e in ppi:
x1,y1 = pos[e['protein1']]; x2,y2 = pos[e['protein2']]
ax.plot([x1,x2],[y1,y2], color='#888', alpha=0.3+0.5*e.get('score',0))
for name,(x,y) in pos.items():
ax.scatter([x],[y], s=450, color='#ffd54f', edgecolors='#333', zorder=3)
ax.annotate(name, (x,y), ha='center', va='center', fontsize=8, fontweight='bold', zorder=4)
ax.set_aspect('equal'); ax.axis('off')
ax.set_title(f'STRING PPI network ({len(ppi)} edges)')
plt.tight_layout(); plt.show()
4. Reactome pathway footprint¶
In [6]:
pw_rows = [{'gene': 'TREM2', 'n_pathways': 4, 'top_pathway': 'Immunoregulatory interactions between a Lymphoid and a non-Lymphoid ce'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)
| gene | n_pathways | top_pathway | |
|---|---|---|---|
| 0 | TREM2 | 4 | Immunoregulatory interactions between a Lympho... |
5. Hypothesis ranking (2 hypotheses)¶
In [7]:
hyp_data = [('Multi-Biomarker Composite Index Surpassing Amyloid PET ', 0.933), ('Dynamic Microglial Phenotype Switching Biomarker Panel ', 0.577)]
titles = [h[0] for h in hyp_data][::-1]
scores = [h[1] for h in hyp_data][::-1]
fig, ax = plt.subplots(figsize=(10, max(8, len(titles)*0.4)))
colors = ['#ef5350' if s >= 0.6 else '#ffa726' if s >= 0.5 else '#66bb6a' for s in scores]
ax.barh(range(len(titles)), scores, color=colors)
ax.set_yticks(range(len(titles))); ax.set_yticklabels(titles, fontsize=7)
ax.set_xlabel('Composite Score'); ax.set_title('What amyloid threshold level is required for optimal clinical benefit in early AD?')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()
In [8]:
labels = ['Multi-Biomarker Composite Index Surpassi', 'Dynamic Microglial Phenotype Switching B']
matrix = np.array([[0.65, 0.92, 0.88, 0.82, 0.0, 0.85, 0.88, 0.95, 0.98], [0.0, 0.0, 0.0, 0.82, 0.0, 0.85, 0.88, 0.95, 0.98]])
dims = ['novelty_score', 'feasibility_score', 'impact_score', 'mechanistic_plausibility_score', 'clinical_relevance_score', 'data_availability_score', 'reproducibility_score', 'druggability_score', 'safety_profile_score']
if matrix.size:
fig, ax = plt.subplots(figsize=(10, 5))
im = ax.imshow(matrix, cmap='RdYlGn', aspect='auto', vmin=0, vmax=1)
ax.set_xticks(range(len(dims)))
ax.set_xticklabels([d.replace('_score','').replace('_',' ').title() for d in dims],
rotation=45, ha='right', fontsize=8)
ax.set_yticks(range(len(labels))); ax.set_yticklabels(labels, fontsize=7)
ax.set_title('Score dimensions — hypotheses')
plt.colorbar(im, ax=ax, shrink=0.8)
plt.tight_layout(); plt.show()
else:
print('No score data available')
6. PubMed literature per hypothesis¶
Hypothesis 1: Multi-Biomarker Composite Index Surpassing Amyloid PET for Treatment R¶
Target genes: COMPOSITE_BIOMARKER · Composite score: 0.933
Plasma p-tau217/CSF neurogranin ratio as a real-time surrogate for amyloid-related treatment effects. Patients with measurable reduction in this ratio within 6 months may demonstrate cognitive benefit, with the magnitude of change potentially reflecting the interaction between tau pathology burden (
In [9]:
print('No PubMed results for hypothesis h-45d23b07')
No PubMed results for hypothesis h-45d23b07
Hypothesis 2: Dynamic Microglial Phenotype Switching Biomarker Panel for Neuroinflam¶
Target genes: TREM2 · Composite score: 0.577
Plasma TREM2/YKL-40 ratio combined with CSF GPNMB levels serves as a dynamic readout of microglial phenotype switching from pro-inflammatory to neuroprotective states during treatment. This composite biomarker captures the transition from DAM (disease-associated microglia) activation to homeostatic
In [10]:
lit_data = [{'year': '2021', 'journal': 'Mech Ageing Dev', 'title': "TREM2, microglia, and Alzheimer's disease.", 'pmid': '33516818'}, {'year': '2023', 'journal': 'Semin Immunol', 'title': 'TREM2: A new player in the tumor microenvironment.', 'pmid': '36989543'}, {'year': '2017', 'journal': 'Cell', 'title': "TREM2 Maintains Microglial Metabolic Fitness in Alzheimer's Disease.", 'pmid': '28802038'}, {'year': '2017', 'journal': 'Trends Mol Med', 'title': 'TREM2, Microglia, and Neurodegenerative Diseases.', 'pmid': '28442216'}, {'year': '2024', 'journal': 'Neuropharmacology', 'title': 'Microglia and TREM2.', 'pmid': '38821351'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2021 | Mech Ageing Dev | TREM2, microglia, and Alzheimer's disease. | 33516818 |
| 1 | 2023 | Semin Immunol | TREM2: A new player in the tumor microenvironm... | 36989543 |
| 2 | 2017 | Cell | TREM2 Maintains Microglial Metabolic Fitness i... | 28802038 |
| 3 | 2017 | Trends Mol Med | TREM2, Microglia, and Neurodegenerative Diseases. | 28442216 |
| 4 | 2024 | Neuropharmacology | Microglia and TREM2. | 38821351 |
7. Knowledge graph edges (4 total)¶
In [11]:
edge_data = [{'source': 'h-45d23b07', 'relation': 'targets', 'target': 'COMPOSITE_BIOMARKER', 'strength': 0.5}, {'source': 'COMPOSITE_BIOMARKER', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'COMPOSITE_BIOMARKER', 'relation': 'implicated_in', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'COMPOSITE_BIOMARKER', 'relation': 'co_associated_with', 'target': 'PET', 'strength': 0.3}]
if edge_data:
pd.DataFrame(edge_data).head(25)
else:
print('No KG edge data available')
Caveats¶
This notebook uses real Forge tool calls from live APIs:
- Enrichment is against curated gene-set libraries (Enrichr)
- STRING/Reactome/HPA/MyGene reflect curated knowledge
- PubMed literature is search-relevance ranked, not systematic review