LDlink is a suite of web-based applications designed to easily and efficiently interrogate linkage disequilibrium in population groups. Each included application is specialized for querying and displaying unique aspects of linkage disequilibrium.

A GitHub compiling the input data, Python and Jupyter Notebook scripts, and all relevant statistical outputs from running the AutoMLPipe-BC automated machine learning pipeline (from the Urbanowicz Lab - https://github.com/UrbsLab) on a large-scale single nucleotide polymorphism (SNP) dataset from patients with congenital heart disease (CHD)

Code repository for my master's thesis project

SNP(VCF format) data preprocessing for GWAS disease prediction

A novel deep polygenic neural network for predicting and identifying yield-associated SNPs in Indonesian rice accessions.

Genetic Ancestry

Facilitates post-processing of SNP pipeline outputs

Application of kernel methods to classify SNP's.

By leveraging ensemble learning, this program can be used to analyze the Linkage Disequilibrium between SNPs in each Indonesian rice chromosomes. Developed using Python 3.9.12.

Simulation of a pair of Single Nucleotide Polymorphisms (SNPs) and associated binary response (e.g., disease status) based on real SNP data in R.

SARS-CoV-2 Clade identifier with SNP data using machine learning.

Evolutionary genomics of shared mutations within the Daphnia pulex species complex.