Graph Neural Networks for Social and Biological Network Analysis

This project introduces students to the cutting-edge field of Graph Neural Networks (GNNs) with a focus on understanding how GNNs can capture and represent complex relationships in non-Euclidean data, such as social and biological networks. Students will explore how GNN-derived numerical embeddings can effectively represent the intricate connections and interactions between individuals or entities for predictive analytics. The use of GNNs will be compared to other state-of-the-art community dete

Automated Bladder Cancer Screening with Deep Learning Algorithms

This project focuses on enhancing bladder cancer screening methods by developing algorithms to analyze cell imagery from urine specimens. The goal is to distinguish between cell nucleus and cytoplasm, using various image segmentation algorithms, as a quantitative marker of malignancy.

Cancer Subtype Multi-Class Classification in Gene Expression Data

This project deals with a multi-class classification task (5 tumor types) within RNA-seq gene expression data. This project will introduce participants to topics such as data cleaning, clustering, feature selection methods, and machine learning modeling.

Modeling Complex Binary-Class Associations in Simulated Genomic Data

This project deals with binary classification tasks (case/control) in a variety of simulated single nucleotide polymorphism (SNP) datasets. Each dataset has a different form of underlying complex association (e.g. multivariate additive, epistatic, or genetic heterogeneity). This project will introduce participants to topics such as basic data preparation, feature selection methods, machine learning (ML) modeling algorithms, automated machine learning, and model explanation and interpretation.