Improving Personalized Prediction of Cancer Prognoses with Clonal Evolution Models
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Updated
Sep 23, 2019 - Python
Improving Personalized Prediction of Cancer Prognoses with Clonal Evolution Models
A deep research study introducing the Gene Drift Hypothesis: a framework 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.
PPTStab: Designing of thermostable proteins with a desired melting temperature
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.
Docker and Apptainer setup for the BEAST 2 software
Experiments with the BEAST 2 software
A bioinformatics pipeline that fits evolutionary models and detects natural selection from multiple sequence alignments
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
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
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