import pandas as pd
ann_rows = [{'gene': 'FFAR2', 'name': 'free fatty acid receptor 2', 'summary': 'This gene encodes a member of the GP40 family of G protein-coupled receptors that are clustered together on chromosome 1'}, {'gene': 'FFAR3', 'name': 'free fatty acid receptor 3', 'summary': 'Enables G protein-coupled receptor activity. Involved in adenylate cyclase-inhibiting G protein-coupled receptor signali'}, {'gene': 'HDAC6', 'name': 'histone deacetylase 6', 'summary': 'Histones play a critical role in transcriptional regulation, cell cycle progression, and developmental events. Histone a'}, {'gene': 'HSP90AA1', 'name': 'heat shock protein 90 alpha family class A member 1', 'summary': 'The protein encoded by this gene is an inducible molecular chaperone that functions as a homodimer. The encoded protein '}, {'gene': 'NLRP3', 'name': 'NLR family pyrin domain containing 3', 'summary': 'This gene encodes a pyrin-like protein containing a pyrin domain, a nucleotide-binding site (NBS) domain, and a leucine-'}]
pd.DataFrame(ann_rows)

go_bp = [{'rank': 1, 'term': 'Cholesterol Import (GO:0070508)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['LDLR']}, {'rank': 2, 'term': 'Negative Regulation Of Astrocyte Differentiation (GO:0048712)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['LDLR']}, {'rank': 3, 'term': 'Positive Regulation Of Protein Catabolic Process In The Vacuole (GO:1904352)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['LDLR']}, {'rank': 4, 'term': 'Negative Regulation Of Lipoprotein Particle Clearance (GO:0010985)', 'p_value': 0.0002999905873105514, 'odds_ratio': 19994.0, 'genes': ['LDLR']}, {'rank': 5, 'term': 'Negative Regulation Of Microglial Cell Activation (GO:1903979)', 'p_value': 0.00034998943191536045, 'odds_ratio': 19993.0, 'genes': ['LDLR']}, {'rank': 6, 'term': 'Regulation Of Lysosomal Protein Catabolic Process (GO:1905165)', 'p_value': 0.0003999883241497325, 'odds_ratio': 19992.0, 'genes': ['LDLR']}, {'rank': 7, 'term': 'Intestinal Cholesterol Absorption (GO:0030299)', 'p_value': 0.0004499872571388355, 'odds_ratio': 19991.0, 'genes': ['LDLR']}, {'rank': 8, 'term': 'Regulation Of Low-Density Lipoprotein Particle Clearance (GO:0010988)', 'p_value': 0.0004999862257578379, 'odds_ratio': 19990.0, 'genes': ['LDLR']}, {'rank': 9, 'term': 'Negative Regulation Of Nitrogen Compound Metabolic Process (GO:0051172)', 'p_value': 0.0005499852259717758, 'odds_ratio': 19989.0, 'genes': ['LDLR']}, {'rank': 10, 'term': 'Intestinal Lipid Absorption (GO:0098856)', 'p_value': 0.0005499852259717758, 'odds_ratio': 19989.0, 'genes': ['LDLR']}]
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']]

import matplotlib.pyplot as plt
import numpy as np

go_bp = [{'rank': 1, 'term': 'Cholesterol Import (GO:0070508)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['LDLR']}, {'rank': 2, 'term': 'Negative Regulation Of Astrocyte Differentiation (GO:0048712)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['LDLR']}, {'rank': 3, 'term': 'Positive Regulation Of Protein Catabolic Process In The Vacuole (GO:1904352)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['LDLR']}, {'rank': 4, 'term': 'Negative Regulation Of Lipoprotein Particle Clearance (GO:0010985)', 'p_value': 0.0002999905873105514, 'odds_ratio': 19994.0, 'genes': ['LDLR']}, {'rank': 5, 'term': 'Negative Regulation Of Microglial Cell Activation (GO:1903979)', 'p_value': 0.00034998943191536045, 'odds_ratio': 19993.0, 'genes': ['LDLR']}, {'rank': 6, 'term': 'Regulation Of Lysosomal Protein Catabolic Process (GO:1905165)', 'p_value': 0.0003999883241497325, 'odds_ratio': 19992.0, 'genes': ['LDLR']}, {'rank': 7, 'term': 'Intestinal Cholesterol Absorption (GO:0030299)', 'p_value': 0.0004499872571388355, 'odds_ratio': 19991.0, 'genes': ['LDLR']}, {'rank': 8, 'term': 'Regulation Of Low-Density Lipoprotein Particle Clearance (GO:0010988)', 'p_value': 0.0004999862257578379, 'odds_ratio': 19990.0, 'genes': ['LDLR']}]
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()

ppi = [{'protein1': 'CETP', 'protein2': 'LDLR', 'score': 0.54, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'APOE', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'APOH', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'APOB', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'CLU', 'score': 0.749, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.142}, {'protein1': 'CETP', 'protein2': 'PON1', 'score': 0.816, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.371}, {'protein1': 'APOH', 'protein2': 'LDLR', 'score': 0.693, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.362, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'APOH', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOH', 'protein2': 'PON1', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOH', 'protein2': 'CLU', 'score': 0.738, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.106}, {'protein1': 'APOH', 'protein2': 'APOE', 'score': 0.775, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.231}, {'protein1': 'APOH', 'protein2': 'APOB', 'score': 0.839, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.451}, {'protein1': 'PON1', 'protein2': 'LDLR', 'score': 0.54, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'PON1', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'PON1', 'protein2': 'APOB', 'score': 0.788, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.277}, {'protein1': 'PON1', 'protein2': 'APOE', 'score': 0.867, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.545}, {'protein1': 'PON1', 'protein2': 'CLU', 'score': 0.984, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0.72, 'tscore': 0.927}, {'protein1': 'APOB', 'protein2': 'DAB2', 'score': 0.499, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'LDLRAP1', 'score': 0.499, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'PCSK9', 'score': 0.649, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.649}, {'protein1': 'APOB', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'CLU', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'LDLR', 'score': 0.937, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.405, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'APOE', 'score': 0.997, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.422, 'dscore': 0.8, 'tscore': 0.982}, {'protein1': 'APOE', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOE', 'protein2': 'LDLR', 'score': 0.783, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.549, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'APOE', 'protein2': 'CLU', 'score': 0.991, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.971}, {'protein1': 'PCSK9', 'protein2': 'LDLR', 'score': 0.995, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.96, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'DAB2', 'protein2': 'LDLRAP1', 'score': 0.8, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.8, 'tscore': 0}]
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)

import math
ppi = [{'protein1': 'CETP', 'protein2': 'LDLR', 'score': 0.54, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'APOE', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'APOH', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'APOB', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'CETP', 'protein2': 'CLU', 'score': 0.749, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.142}, {'protein1': 'CETP', 'protein2': 'PON1', 'score': 0.816, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.371}, {'protein1': 'APOH', 'protein2': 'LDLR', 'score': 0.693, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.362, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'APOH', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOH', 'protein2': 'PON1', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOH', 'protein2': 'CLU', 'score': 0.738, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.106}, {'protein1': 'APOH', 'protein2': 'APOE', 'score': 0.775, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.231}, {'protein1': 'APOH', 'protein2': 'APOB', 'score': 0.839, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.451}, {'protein1': 'PON1', 'protein2': 'LDLR', 'score': 0.54, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'PON1', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'PON1', 'protein2': 'APOB', 'score': 0.788, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.277}, {'protein1': 'PON1', 'protein2': 'APOE', 'score': 0.867, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.545}, {'protein1': 'PON1', 'protein2': 'CLU', 'score': 0.984, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0.72, 'tscore': 0.927}, {'protein1': 'APOB', 'protein2': 'DAB2', 'score': 0.499, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'LDLRAP1', 'score': 0.499, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'PCSK9', 'score': 0.649, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.649}, {'protein1': 'APOB', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'CLU', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'LDLR', 'score': 0.937, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.405, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'APOB', 'protein2': 'APOE', 'score': 0.997, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.422, 'dscore': 0.8, 'tscore': 0.982}, {'protein1': 'APOE', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'APOE', 'protein2': 'LDLR', 'score': 0.783, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.549, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'APOE', 'protein2': 'CLU', 'score': 0.991, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.971}, {'protein1': 'PCSK9', 'protein2': 'LDLR', 'score': 0.995, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.96, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'DAB2', 'protein2': 'LDLRAP1', 'score': 0.8, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.8, 'tscore': 0}, {'protein1': 'DAB2', 'protein2': 'LDLR', 'score': 0.926, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'CLU', 'protein2': 'LDLR', 'score': 0.576, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.54, 'tscore': 0.117}, {'protein1': 'CLU', 'protein2': 'HPR', 'score': 0.72, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0}, {'protein1': 'LDLRAP1', 'protein2': 'LDLR', 'score': 0.964, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.543, 'dscore': 0.9, 'tscore': 0.288}, {'protein1': 'HPR', 'protein2': 'LDLR', 'score': 0.542, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.54, 'tscore': 0.047}]
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()

pw_rows = [{'gene': 'FFAR2', 'n_pathways': 2, 'top_pathway': 'G alpha (q) signalling events'}, {'gene': 'FFAR3', 'n_pathways': 2, 'top_pathway': 'G alpha (q) signalling events'}, {'gene': 'HDAC6', 'n_pathways': 8, 'top_pathway': 'NOTCH1 Intracellular Domain Regulates Transcription'}, {'gene': 'HSP90AA1', 'n_pathways': 8, 'top_pathway': 'Signaling by ERBB2'}, {'gene': 'NLRP3', 'n_pathways': 6, 'top_pathway': 'Metalloprotease DUBs'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)

hyp_data = [('HDAC6 Activation as SCFA-Mediated Neuroprotective Mecha', 0.682), ("Dissociating SCFA's Dual Signaling Through GPR43/GPR41 ", 0.67)]
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('Do SCFAs directly modulate α-synuclein aggregation in vivo at physiologically relevant brain concentrations?')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['HDAC6 Activation as SCFA-Mediated Neurop', "Dissociating SCFA's Dual Signaling Throu"]
matrix = np.array([[0.82, 0.32, 0.65, 0.75, 0.0, 0.55, 0.6, 0.28, 0.42], [0.75, 0.38, 0.6, 0.58, 0.0, 0.48, 0.52, 0.45, 0.55]])
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')

print('No PubMed results for hypothesis h-ca454967')

print('No PubMed results for hypothesis h-ded1734f')