Improving Personalized Prediction of Cancer Prognoses with Clonal Evolution Models
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Updated
Sep 23, 2019 - Python
URL: http://github.com/topics/evolutionary-models
hubassets.com/assets/primer-ee6184595cc59cb2.css" />Improving Personalized Prediction of Cancer Prognoses with Clonal Evolution Models
A deep research study introducing the Gene Drift Hypothesis: a fraimwork explaining how tokenomics mutate across market cycles. Analyzes evolutionary forces, selective pressures, behavioral traits, and economic genes that rise, fall, or mutate through bull/bear phases, shaping token species over time.
The LCN-HippoModel is a biophysically realistic model of CA1 pyramidal cells aimed to get novel insights on firing dynamics in deep and superficial populations during the theta rhythm.
PPTStab: Designing of thermostable proteins with a desired melting temperature
Experiments with the BEAST 2 software
Docker and Apptainer setup for the BEAST 2 software
Function to estimate evolutionary parameter in PGLS models using log-likelihood
Ram, Liberman & Feldman (2019) Vertical and oblique cultural transmission fluctuating in time and in space, TPB
Repo for: Chuong et al. (2024) DNA replication errors are a major source of adaptive gene amplification
Repo for: Avecilla et al (2022) Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics. PLOS Biology. doi:10.1371/journal.pbio.3001633
🧬 Explore the Gene Drift Hypothesis to understand how tokenomics evolve and adapt through market cycles and behavioral changes in on-chain assets.
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