import pandas as pd
ann_rows = [{'gene': 'C9ORF72', 'name': 'C9orf72-SMCR8 complex subunit', 'summary': 'The protein encoded by this gene plays an important role in the regulation of endosomal trafficking, and has been shown '}, {'gene': 'MCOLN1', 'name': 'mucolipin TRP cation channel 1', 'summary': 'This gene encodes a memberof the transient receptor potential (TRP) cation channel gene family. The transmembrane protei'}, {'gene': 'PIKFYVE', 'name': 'phosphoinositide kinase, FYVE-type zinc finger containing', 'summary': 'Phosphorylated derivatives of phosphatidylinositol (PtdIns) regulate cytoskeletal functions, membrane trafficking, and r'}, {'gene': 'PPP3CB', 'name': 'protein phosphatase 3 catalytic subunit beta', 'summary': 'Enables several functions, including calmodulin binding activity; calmodulin-dependent protein phosphatase activity; and'}, {'gene': 'RAB7A', 'name': 'RAB7A, member RAS oncogene family', 'summary': 'RAB family members are small, RAS-related GTP-binding proteins that are important regulators of vesicular transport. Eac'}, {'gene': 'SMCR8', 'name': 'SMCR8-C9orf72 complex subunit', 'summary': 'Enables protein kinase binding activity and protein kinase inhibitor activity. Contributes to guanyl-nucleotide exchange'}, {'gene': 'TFEB', 'name': 'transcription factor EB', 'summary': 'Enables DNA-binding transcription factor activity; enzyme binding activity; and transcription cis-regulatory region bind'}]
pd.DataFrame(ann_rows)

go_bp = [{'rank': 1, 'term': 'Phagosome Maturation (GO:0090382)', 'p_value': 1.1922157015969443e-08, 'odds_ratio': 1248.8125, 'genes': ['PIKFYVE', 'MCOLN1', 'RAB7A']}, {'rank': 2, 'term': 'Regulation Of Autophagosome Assembly (GO:2000785)', 'p_value': 2.5792382404323026e-07, 'odds_ratio': 404.5135135135135, 'genes': ['PIKFYVE', 'SMCR8', 'C9ORF72']}, {'rank': 3, 'term': 'Regulation Of Lysosome Organization (GO:1905671)', 'p_value': 1.0494326119170083e-06, 'odds_ratio': 2665.3333333333335, 'genes': ['PPP3CB', 'MCOLN1']}, {'rank': 4, 'term': 'Negative Regulation Of Exocytosis (GO:0045920)', 'p_value': 4.71859981396871e-06, 'odds_ratio': 999.25, 'genes': ['SMCR8', 'RAB7A']}, {'rank': 5, 'term': 'Phagosome-Lysosome Fusion (GO:0090385)', 'p_value': 5.766229498988049e-06, 'odds_ratio': 888.1777777777778, 'genes': ['PIKFYVE', 'RAB7A']}, {'rank': 6, 'term': 'Phagolysosome Assembly (GO:0001845)', 'p_value': 9.535771922855927e-06, 'odds_ratio': 666.0333333333333, 'genes': ['PIKFYVE', 'RAB7A']}, {'rank': 7, 'term': 'Regulation Of Vacuole Organization (GO:0044088)', 'p_value': 1.4244198490555207e-05, 'odds_ratio': 532.7466666666667, 'genes': ['PIKFYVE', 'C9ORF72']}, {'rank': 8, 'term': 'Positive Regulation Of Vacuole Organization (GO:0044090)', 'p_value': 2.198015227094698e-05, 'odds_ratio': 420.5052631578947, 'genes': ['PPP3CB', 'MCOLN1']}, {'rank': 9, 'term': 'Axon Extension (GO:0048675)', 'p_value': 2.6472069779530597e-05, 'odds_ratio': 380.4190476190476, 'genes': ['PPP3CB', 'C9ORF72']}, {'rank': 10, 'term': 'Endocytosis (GO:0006897)', 'p_value': 2.8270157200566938e-05, 'odds_ratio': 79.86693548387096, 'genes': ['PIKFYVE', 'C9ORF72', 'RAB7A']}]
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': 'Phagosome Maturation (GO:0090382)', 'p_value': 1.1922157015969443e-08, 'odds_ratio': 1248.8125, 'genes': ['PIKFYVE', 'MCOLN1', 'RAB7A']}, {'rank': 2, 'term': 'Regulation Of Autophagosome Assembly (GO:2000785)', 'p_value': 2.5792382404323026e-07, 'odds_ratio': 404.5135135135135, 'genes': ['PIKFYVE', 'SMCR8', 'C9ORF72']}, {'rank': 3, 'term': 'Regulation Of Lysosome Organization (GO:1905671)', 'p_value': 1.0494326119170083e-06, 'odds_ratio': 2665.3333333333335, 'genes': ['PPP3CB', 'MCOLN1']}, {'rank': 4, 'term': 'Negative Regulation Of Exocytosis (GO:0045920)', 'p_value': 4.71859981396871e-06, 'odds_ratio': 999.25, 'genes': ['SMCR8', 'RAB7A']}, {'rank': 5, 'term': 'Phagosome-Lysosome Fusion (GO:0090385)', 'p_value': 5.766229498988049e-06, 'odds_ratio': 888.1777777777778, 'genes': ['PIKFYVE', 'RAB7A']}, {'rank': 6, 'term': 'Phagolysosome Assembly (GO:0001845)', 'p_value': 9.535771922855927e-06, 'odds_ratio': 666.0333333333333, 'genes': ['PIKFYVE', 'RAB7A']}, {'rank': 7, 'term': 'Regulation Of Vacuole Organization (GO:0044088)', 'p_value': 1.4244198490555207e-05, 'odds_ratio': 532.7466666666667, 'genes': ['PIKFYVE', 'C9ORF72']}, {'rank': 8, 'term': 'Positive Regulation Of Vacuole Organization (GO:0044090)', 'p_value': 2.198015227094698e-05, 'odds_ratio': 420.5052631578947, 'genes': ['PPP3CB', 'MCOLN1']}]
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': 'TFEB', 'protein2': 'PPP3CB', 'score': 0.483, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.301}, {'protein1': 'SMCR8', 'protein2': 'C9orf72', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.96, 'dscore': 0.8, 'tscore': 0.983}]
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': 'TFEB', 'protein2': 'PPP3CB', 'score': 0.483, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.301}, {'protein1': 'SMCR8', 'protein2': 'C9orf72', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.96, 'dscore': 0.8, 'tscore': 0.983}]
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': 'C9ORF72', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'MCOLN1', 'n_pathways': 2, 'top_pathway': 'TRP channels'}, {'gene': 'PIKFYVE', 'n_pathways': 3, 'top_pathway': 'Synthesis of PIPs at the Golgi membrane'}, {'gene': 'PPP3CB', 'n_pathways': 6, 'top_pathway': 'DARPP-32 events'}, {'gene': 'RAB7A', 'n_pathways': 8, 'top_pathway': 'MHC class II antigen presentation'}, {'gene': 'SMCR8', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'TFEB', 'n_pathways': 2, 'top_pathway': 'Transcriptional and post-translational regulation of MITF-M expression'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)

hyp_data = [('PIKFYVE Inhibition Activates Aggregate Exocytosis via P', 0.584), ('C9orf72-SMCR8-WDR41 Complex Dysfunction in C9-ALS Rescu', 0.562)]
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('How does PIKFYVE inhibition activate unconventional protein clearance via exocytosis?')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['PIKFYVE Inhibition Activates Aggregate E', 'C9orf72-SMCR8-WDR41 Complex Dysfunction ']
matrix = np.array([[0.8, 0.6, 0.55, 0.65, 0.0, 0.7, 0.6, 0.75, 0.45], [0.75, 0.5, 0.55, 0.55, 0.0, 0.55, 0.55, 0.65, 0.45]])
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-9025d807')

print('No PubMed results for hypothesis h-b3e97952')