Model-based subclonal deconvolution from bulk sequencing.
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Updated
Nov 9, 2023 - HTML
Model-based subclonal deconvolution from bulk sequencing.
R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model
**Unsupervised-Learning**(with practice of PCA, ICA and Model-based Clustering)
R Package to Perform Clustering of Three-way Count Data Using Mixtures of Matrix Variate Poisson-log Normal Model With Parameter Estimation via MCMC-EM, Variational Gaussian Approximations, or a Hybrid Approach Combining Both.
Unsupervised Learning
Infinite Mixtures of Infinite Factor Analysers
Gaussian Parsimonious Clustering Models with Gating and Expert Network Covariates
Mixtures of Exponential-Distance Models for Clustering Longitudinal Life-Course Sequences with Gating Covariates and Sampling Weights
This project is an extension of the Gaussian Mixture Regression (GMR) model to handel censored multivariate responses.
This code is part of the "Comparison of K-Means and Model-Based Clustering methods for drill core pseudo-log generation based on X-Ray Fluorescence Data" written by researchers of the Directory of Geology and Mineral Resources from the Geological Survey of Brazil – CPRM.
R & Python | Unsupervised Learning Project
A Predictive View of Bayesian Clustering
Hierarchical, model-based, and density-based clustering in R and application to unsupervised country classification
EMMIX fits the data into the specified multivariate mixture models via the EM Algorithm.
Python code to fit parsimonious Markov models
VEV model from Mclust among 5 clustering algorithms has optimal performance and detected 8 distinct groups of users. Data was cleaned, standardized and feature-selected, PCA’s biplot, Ggplot, Radar plots, and parallel coordinate plots were applied for EDA.
Bayesian Specification of model-based clustering
Processing DNA Copy Number (CN) Data for Detection of CN Events
Implementation of mixture model, parameters estimated by EM algorithm. ~/Goto/Project/Page/👇
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