Editing Personalized Medicine (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Multi-omics patient stratification with autoencoders:'''
<syntaxhighlight lang="python">
import torch
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

class MultiOmicsAutoencoder(nn.Module):
    """Integrate genomics + transcriptomics + proteomics for patient stratification."""
    def __init__(self, dims_in: dict, latent_dim=64):
        super().__init__()
        # Separate encoders per omics type
        self.encoders = nn.ModuleDict({
            omic: nn.Sequential(
                nn.Linear(dim, 256), nn.ReLU(), nn.BatchNorm1d(256),
                nn.Linear(256, 128), nn.ReLU()
            ) for omic, dim in dims_in.items()
        })
        # Joint latent space
        n_omics = len(dims_in)
        self.joint_encoder = nn.Sequential(
            nn.Linear(128 * n_omics, latent_dim), nn.ReLU()
        )
        # Decoders (one per omics)
        self.decoders = nn.ModuleDict({
            omic: nn.Sequential(
                nn.Linear(latent_dim, 128), nn.ReLU(),
                nn.Linear(128, 256), nn.ReLU(),
                nn.Linear(256, dim)
            ) for omic, dim in dims_in.items()
        })

    def forward(self, data: dict):
        # Encode each omics modality
        encoded = [self.encoders[k](v) for k, v in data.items()]
        joint = torch.cat(encoded, dim=1)
        z = self.joint_encoder(joint)
        # Reconstruct each modality from shared latent
        reconstructed = {k: self.decoders[k](z) for k in data.keys()}
        return z, reconstructed

def stratify_patients(z: np.ndarray, n_clusters=4):
    """Cluster patients in latent space → subtypes with different treatment response."""
    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
    subtypes = kmeans.fit_predict(z)
    return subtypes  # Each subtype may require different treatment

# Usage: load TCGA multi-omics data
dims = {'rna_seq': 20000, 'copy_number': 10000, 'methylation': 5000}
model = MultiOmicsAutoencoder(dims_in=dims, latent_dim=64)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

def train_step(data: dict):
    z, reconstructed = model(data)
    loss = sum(F.mse_loss(reconstructed[k], data[k]) for k in data)
    optimizer.zero_grad(); loss.backward(); optimizer.step()
    return z.detach().numpy(), loss.item()
</syntaxhighlight>

; Personalized medicine AI tools
: '''Pharmacogenomics''' → GeneSight, Translational Software, CPIC clinical decision support
: '''Cancer personalization''' → Foundation One CDx + treatment matching, Tempus, Flatiron
: '''Multi-omics integration''' → MOFA+ (R/Python), MINT, mixOmics
: '''Digital twins''' → Unlearn.ai (clinical trial), Dassault Systèmes Living Heart Model
: '''PRS-based prevention''' → Genomic Health Allelica, AncestryHealth, Color Health
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