Full Curriculum

from Bio import SeqIO import pandas as pd records = list(SeqIO.parse("genome.fasta","fasta")) df = pd.DataFrame([{ "id": r.id, "gc": (r.seq.count("G")+r.seq.count("C"))/len(r.seq), "length": len(r.seq) } for r in records]) print(df.describe())

from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import StratifiedKFold clf = RandomForestClassifier(n_estimators=200) cv = StratifiedKFold(n_splits=5) scores = cross_val_score(clf, X, y, cv=cv) print(f"F1: {scores.mean():.3f}")

from sklearn.decomposition import PCA import matplotlib.pyplot as plt pca = PCA(n_components=2) pcs = pca.fit_transform(scaled_expr) plt.scatter(pcs[:,0], pcs[:,1], c=tissue_labels) plt.xlabel(f"PC1 ({pca.explained_variance_ratio_[0]:.0%})") plt.savefig("pca_tissues.png", dpi=150)

class DNAClassifier(nn.Module): def __init__(self): super().__init__() self.embed = nn.Embedding(5, 64) self.conv = nn.Conv1d(64,128, kernel_size=8) self.pool = nn.AdaptiveMaxPool1d(32) self.head = nn.Linear(128*32, 5) def forward(self, x): x = self.embed(x).permute(0,2,1) return self.head(self.pool( torch.relu(self.conv(x))).flatten(1))

import numpy as np def mutual_info(col_i, col_j): # Coevolution between MSA columns ... L = len(msa[0]) mi_mat = np.zeros((L, L)) for i in range(L): for j in range(i+1, L): mi_mat[i,j] = mutual_info( [s[i] for s in msa], [s[j] for s in msa])

def anomaly_score(model, image): model.eval() with torch.no_grad(): recon = model(image.unsqueeze(0)) mse = F.mse_loss(recon.squeeze(), image) return mse.item() # High MSE → likely anomaly / pathology

from torch_geometric.nn import GCNConv from torch_geometric.nn import global_mean_pool class MolGNN(nn.Module): def __init__(self): super().__init__() self.conv1 = GCNConv(9, 64) self.conv2 = GCNConv(64, 64) self.head = nn.Linear(64, 1) def forward(self, data): x = F.relu(self.conv1(data.x, data.edge_index)) x = global_mean_pool(x, data.batch) return self.head(x)

class MolVAE(nn.Module): def reparameterize(self, mu, logvar): std = torch.exp(0.5 * logvar) eps = torch.randn_like(std) return mu + eps * std # sample z~N(mu,sigma) # Sample novel molecules: z = torch.randn(16, 256) # batch of 16 latents # → decode → valid SMILES strings

from fastapi import FastAPI app = FastAPI(title="Genome Classifier v2") @app.post("/classify") async def classify(req: SeqRequest): features = extract_kmer(req.sequence, k=4) pred = model.predict([features]) return {"organism": pred[0], "confidence": 0.97}