Robust Machine Learning Algorithms: We leverage powerful models such as Generalized Linear Models (GLM), Decision Trees, Random Forests, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naive Bayes to analyze complex genomic datasets, and Deep Learning.

Applications include:

Classification & Prediction: Disease susceptibility, phenotype classification, and genotype-to-phenotype predictions

Feature Selection: Identification of informative genetic markers (e.g., SNPs, gene expression profiles)

Clustering & Pattern Discovery: Grouping of samples based on molecular signatures or hidden structures in the data

Deliverables:

High-quality visualizations (confusion matrices, ROC curves, feature importance plots)

Annotated result tables highlighting predictive markers and model performance metrics

Custom analyses tailored to your research questions and metadata (e.g., clinical, environmental, or geographic variables)