Presentation date: April 18, 2024

Presenter: NDAGIJIMANA Frank Aimee Rodrigue

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## PaCMAP-embedded convolutional neural network for multi-omics data integration

## SUPREME: multiomics data integration using graph convolutional networks

## Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations

## Multi-omics data integration by generative adversarial network

## scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data

## Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data

## DeepPINK: reproducible feature selection in deep neural networks

## PGEE-M

### What is PGEE_M?

### Sample Dataset

### Contact

### Download

## COVID-19 Nomogram

## AucPR

## Introduction

## Usage

## Download

Presentation date: April 18, 2024

Presenter: NDAGIJIMANA Frank Aimee Rodrigue

Presentation date: April 04, 2024

Presenter: Jun Sik Kim

Penalized Generalized Estimating Equation of Multinomial Responses is a method for identifying important variables and estimation of their regression coefficient simultaneously for high-dimensional longitudinal multinomial responses.

For variable selection as well as for the estimation of high-dimensional longitudinal data, PGEE_M uses two non-convex penalties such as the SCAD and the MCP penalty.

To estimate model parameters, PGEE_M adopts an iterative algorithm, which combines with the minorization-maximization (MM) algorithm for the nonconvex penalty with the Fisher-scoring algorithm.

Detailed algorithm is described in the below original article: “Penalized generalized estimating equations approach to longitudinal data with multinomial responses”.

This PGEE_M software can only produce results for independent correlation structure.

To create the PGEE_M software, we used some part of code from the “PGEE” package (https://github.com/cran/PGEE) and the “multgee” package (https://github.com/AnestisTouloumis/multgee).

The sample dataset contains 500 samples, with each subject being evaluated at 4 different time points, a total of 100 covariates and the number of categories of response variable is 5.

The PGEE_M program has developed and maintained by

- Md. Kamruzzaman (kzaman1@isrt.ac.bd) at Bioinformatics and Biostatistics Lab., Dept. of Statistics in Seoul National University.

An example R script is linked to here

An example data is linked to here

This fold includes R source files for implementation of the numerical study in the manuscript submitted to Bioinformatics, 2014, titled “AucPR: An AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.”.

The following source codes are included:

**AucPR.R** —— List all functions needed.

**Simu_Setting.R** —— Generate setting for simulations.

**Case_Study.R** ——- A simulation study and a real example study are considered.

**mhsauc_tgdr.f90** —– The fortran code to implement Ma & Huang’s method (MSauc), and mhsauc_tgdr.dll is its dll version. We call it from R.

For detail, please see the R codes.

You can download a zipped file contains source codes and this README from this link : Codes_Penalized_AUC