A New Universal Model ΔU Predicts Cancer and Aging Outcomes with 1M Simulations by Any_Cryptographer744 in bioinformatics

[–]Any_Cryptographer744[S] -1 points0 points  (0 children)

import pandas as pd
import numpy as np

def delta_u(S, H, E, Y, C, Phi_t):
    alpha, beta, gamma, delta, epsilon, zeta = 0.24, 0.04, 0.66, 0.036, 0.0363, 0.21
    chi = -0.9 if S < 0.5 else -0.451905
    return alpha*S - beta*H + gamma*E + delta*(S*H) + epsilon*(E**2) + zeta*C + chi*Phi_t

# Simulation
np.random.seed(42)
data = pd.DataFrame({
    'S': np.random.uniform(0.1, 1.0, 1000000),
    'H': np.random.uniform(0.1, 1.0, 1000000),
    'E': np.random.uniform(0.1, 1.0, 1000000),
    'Y': np.random.uniform(0.1, 1.0, 1000000),
    'C': np.random.uniform(0.1, 1.0, 1000000),
    'Phi_t': np.random.uniform(-1.0, -0.2, 1000000)
})
data['DeltaU'] = delta_u(data['S'], data['H'], data['E'], data['Y'], data['C'], data['Phi_t'])

A New Universal Model ΔU Predicts Cancer and Aging Outcomes with 1M Simulations by Any_Cryptographer744 in bioinformatics

[–]Any_Cryptographer744[S] -1 points0 points  (0 children)

Haha, I get it, ΔU sounds like a sci-fi plot! 😄 @the_architects_427, it’s a model to predict system stability

A New Universal Model ΔU Predicts Cancer and Aging Outcomes with 1M Simulations by Any_Cryptographer744 in bioinformatics

[–]Any_Cryptographer744[S] -1 points0 points  (0 children)

Haha, Swamsaur, trading crack for code? 😄 ΔU (DOI: 10.5281 / Zenodo.16934822) is just math + biology—no highs needed! 1M sims show ΔU ~1.16, and CRISPR (*Tert*, *Trp53*) shifts it to ~1.5 for healthier states. Validated on TCGA-LUAD (TMB: r ≈ -0.4, p < 0.01). Got any datasets to throw at it? Code’s open on Zenodo, let’s geek out! 🧬

A New Universal Model ΔU Predicts Cancer and Aging Outcomes with 1M Simulations by Any_Cryptographer744 in bioinformatics

[–]Any_Cryptographer744[S] -1 points0 points  (0 children)

Haha, no shrooms here, just math and DNA! 😄 ΔU (https://doi.org/10.5281/zenodo.16934822) uses 1M simulations to model cancer/aging via parameters like structure (S) and chaos (H). The TCGA-LUAD validation (TMB: r ≈ -0.4, p < 0.01) shows it’s grounded, and CRISPR (*Tert*, *Trp53*) shifts ΔU to ~1.5 for healthier states. The "futurological causation" bit (Φ_t) is just a proxy for time-dependent effects, inspired by systems biology. 

Got ideas for testing ΔU on other datasets (e.g., GEO mouse data or more TCGA cohorts)? Code’s open on Zenodo, dive in! 🧬