import pandas as pd
ann_rows = [{'gene': 'LDHA', 'name': 'lactate dehydrogenase A', 'summary': 'This gene encodes the A subunit of lactate dehydrogenase enzyme which catalyzes the reversible conversion of pyruvate to'}, {'gene': 'TFAM', 'name': 'transcription factor A, mitochondrial', 'summary': 'This gene encodes a key mitochondrial transcription factor containing two high mobility group motifs. The encoded protei'}, {'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)

go_bp = [{'rank': 1, 'term': 'Chemokine (C-X-C Motif) Ligand 12 Signaling Pathway (GO:0038146)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 2, 'term': 'Negative Regulation Of Sequestering Of Triglyceride (GO:0010891)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 3, 'term': 'Negative Regulation Of Toll-Like Receptor 2 Signaling Pathway (GO:0034136)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 4, 'term': 'Regulation Of Resting Membrane Potential (GO:0060075)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 5, 'term': 'Positive Regulation Of Lipoprotein Particle Clearance (GO:0010986)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 6, 'term': 'Regulation Of Fat Cell Proliferation (GO:0070344)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 7, 'term': 'Negative Regulation Of Astrocyte Differentiation (GO:0048712)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 8, 'term': 'Positive Regulation Of Microglial Cell Activation (GO:1903980)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 9, 'term': 'Positive Regulation Of Microglial Cell Migration (GO:1904141)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 10, 'term': 'Positive Regulation Of Amyloid-Beta Clearance (GO:1900223)', 'p_value': 0.0008997538788767587, 'odds_ratio': 1999.2, 'genes': ['TREM2']}]
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': 'Chemokine (C-X-C Motif) Ligand 12 Signaling Pathway (GO:0038146)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 2, 'term': 'Negative Regulation Of Sequestering Of Triglyceride (GO:0010891)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 3, 'term': 'Negative Regulation Of Toll-Like Receptor 2 Signaling Pathway (GO:0034136)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 4, 'term': 'Regulation Of Resting Membrane Potential (GO:0060075)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 5, 'term': 'Positive Regulation Of Lipoprotein Particle Clearance (GO:0010986)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 6, 'term': 'Regulation Of Fat Cell Proliferation (GO:0070344)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 7, 'term': 'Negative Regulation Of Astrocyte Differentiation (GO:0048712)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}, {'rank': 8, 'term': 'Positive Regulation Of Microglial Cell Activation (GO:1903980)', 'p_value': 0.0007498314718703743, 'odds_ratio': 2499.125, 'genes': ['TREM2']}]
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()

print('No STRING PPI data available')

pw_rows = [{'gene': 'LDHA', 'n_pathways': 2, 'top_pathway': 'Pyruvate metabolism'}, {'gene': 'TFAM', 'n_pathways': 3, 'top_pathway': 'Mitochondrial transcription initiation'}, {'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)

hyp_data = [('Lactate/Pyruvate Ratio Dynamics in CSF as a Neuroinflam', 0.665), ('Mitochondrial Biogenesis Rate as a Dynamic Biomarker of', 0.652)]
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('Which metabolic biomarkers can distinguish therapeutic response from disease progression in neurodegeneration trials?')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['Lactate/Pyruvate Ratio Dynamics in CSF a', 'Mitochondrial Biogenesis Rate as a Dynam']
matrix = np.array([[0.6, 0.8, 0.7, 0.7, 0.0, 0.7, 0.8, 0.8, 0.9], [0.8, 0.4, 0.7, 0.8, 0.0, 0.4, 0.5, 0.5, 0.7]])
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-5afacdfe')

print('No PubMed results for hypothesis h-28d5b559')