import pandas as pd
ann_rows = [{'gene': 'GDNF', 'name': 'glial cell derived neurotrophic factor', 'summary': 'This gene encodes a secreted ligand of the TGF-beta (transforming growth factor-beta) superfamily of proteins. Ligands o'}, {'gene': 'STMN2', 'name': 'stathmin 2', 'summary': 'This gene encodes a member of the stathmin family of phosphoproteins. Stathmin proteins function in microtubule dynamics'}]
pd.DataFrame(ann_rows)

go_bp = [{'rank': 1, 'term': 'Protein Kinase B Signaling (GO:0043491)', 'p_value': 1.3769525171482697e-05, 'odds_ratio': 605.2424242424242, 'genes': ['TYRO3', 'GAS6']}, {'rank': 2, 'term': 'Apoptotic Cell Clearance (GO:0043277)', 'p_value': 1.8850777419928716e-05, 'odds_ratio': 512.025641025641, 'genes': ['TYRO3', 'GAS6']}, {'rank': 3, 'term': 'Phagocytosis (GO:0006909)', 'p_value': 0.00011651719460226656, 'odds_ratio': 198.28855721393035, 'genes': ['TYRO3', 'GAS6']}, {'rank': 4, 'term': 'Positive Regulation Of Kinase Activity (GO:0033674)', 'p_value': 0.0002650830977784653, 'odds_ratio': 130.01960784313727, 'genes': ['TYRO3', 'GAS6']}, {'rank': 5, 'term': 'Endocytosis (GO:0006897)', 'p_value': 0.0008718252945615399, 'odds_ratio': 70.61675579322637, 'genes': ['TYRO3', 'GAS6']}, {'rank': 6, 'term': 'B Cell Chemotaxis (GO:0035754)', 'p_value': 0.0012494733811348967, 'odds_ratio': 1249.4375, 'genes': ['GAS6']}, {'rank': 7, 'term': 'Regulation Of Glomerular Filtration (GO:0003093)', 'p_value': 0.0012494733811348967, 'odds_ratio': 1249.4375, 'genes': ['GAS6']}, {'rank': 8, 'term': 'Positive Regulation Of Phosphorylation (GO:0042327)', 'p_value': 0.0012981262690588075, 'odds_ratio': 57.542940320232894, 'genes': ['TYRO3', 'GAS6']}, {'rank': 9, 'term': 'Receptor-Mediated Virion Attachment To Host Cell (GO:0046813)', 'p_value': 0.0014992198216206949, 'odds_ratio': 999.5, 'genes': ['GAS6']}, {'rank': 10, 'term': 'Negative Regulation Of Dendritic Cell Apoptotic Process (GO:2000669)', 'p_value': 0.0014992198216206949, 'odds_ratio': 999.5, 'genes': ['GAS6']}]
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': 'Protein Kinase B Signaling (GO:0043491)', 'p_value': 1.3769525171482697e-05, 'odds_ratio': 605.2424242424242, 'genes': ['TYRO3', 'GAS6']}, {'rank': 2, 'term': 'Apoptotic Cell Clearance (GO:0043277)', 'p_value': 1.8850777419928716e-05, 'odds_ratio': 512.025641025641, 'genes': ['TYRO3', 'GAS6']}, {'rank': 3, 'term': 'Phagocytosis (GO:0006909)', 'p_value': 0.00011651719460226656, 'odds_ratio': 198.28855721393035, 'genes': ['TYRO3', 'GAS6']}, {'rank': 4, 'term': 'Positive Regulation Of Kinase Activity (GO:0033674)', 'p_value': 0.0002650830977784653, 'odds_ratio': 130.01960784313727, 'genes': ['TYRO3', 'GAS6']}, {'rank': 5, 'term': 'Endocytosis (GO:0006897)', 'p_value': 0.0008718252945615399, 'odds_ratio': 70.61675579322637, 'genes': ['TYRO3', 'GAS6']}, {'rank': 6, 'term': 'B Cell Chemotaxis (GO:0035754)', 'p_value': 0.0012494733811348967, 'odds_ratio': 1249.4375, 'genes': ['GAS6']}, {'rank': 7, 'term': 'Regulation Of Glomerular Filtration (GO:0003093)', 'p_value': 0.0012494733811348967, 'odds_ratio': 1249.4375, 'genes': ['GAS6']}, {'rank': 8, 'term': 'Positive Regulation Of Phosphorylation (GO:0042327)', 'p_value': 0.0012981262690588075, 'odds_ratio': 57.542940320232894, 'genes': ['TYRO3', 'GAS6']}]
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': 'TYRO3', 'protein2': 'GAS6', 'score': 0.989, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, '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)

import math
ppi = [{'protein1': 'TYRO3', 'protein2': 'GAS6', 'score': 0.989, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, '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()

pw_rows = [{'gene': 'GDNF', 'n_pathways': 4, 'top_pathway': 'NCAM1 interactions'}, {'gene': 'STMN2', 'n_pathways': 1, 'top_pathway': 'RND1 GTPase cycle'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)

hyp_data = [('STMN2 Restoration as a Prerequisite for Axon Growth Aft', 0.683), ('GDNF Gradient Establishment by Schwann Cells Enables Mo', 0.61)]
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 molecular mechanisms enable functional recovery and muscle re-innervation after motor neuron loss in ALS/FTLD?')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['STMN2 Restoration as a Prerequisite for ', 'GDNF Gradient Establishment by Schwann C']
matrix = np.array([[0.7, 0.85, 0.82, 0.8, 0.0, 0.82, 0.75, 0.88, 0.68], [0.45, 0.7, 0.78, 0.82, 0.0, 0.78, 0.72, 0.8, 0.6]])
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-2ca830cd')

print('No PubMed results for hypothesis h-6d411c20')