Michael C
- Research Program Mentor
PhD at University of California San Diego (UCSD)
Expertise
genetics, cellular/molecular biology, computer science, neuroscience
Bio
I am a Scientist at the Allen Institute for Brain Science in Seattle, where I study the cellular and genetic basis of Alzheimer's disease as part of the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD). I hold a PhD from the Bioinformatics and Systems Biology Graduate Program at UC San Diego, where I was a National Science Foundation Graduate Research Fellow. I am passionate about understanding human disease, and I strongly believe that biomedical research using DNA sequencing and related technologies can advance that goal. Collaboration and interdisciplinary research are central to my approach, and I am dedicated to using reproducible, rigorous, and data-driven methods to make scientific discoveries. Outside the lab, I'm embracing everything the Pacific Northwest has to offer through hiking, skiing, trail running, and tennis, taking full advantage of the region's mountains and trails with an active lifestyle. I also find joy in cooking and experimenting with new recipes, drawing inspiration from cuisines around the world, especially Italian. These activities provide balance to my work and keep me energized and creative.Project ideas
Diagnosing Breast Cancer Subtypes Using Cancer Genome Sequencing Data
I specialize in disease biology, genomics, bioinformatics, and data analysis. I can help students explore topics such as cancer genomics, bioinformatics techniques, and the application of machine learning in healthcare. Knowledge and Skills to be Learned • Basics of cancer biology, particularly breast cancer subtypes • Principles of genome sequencing and data interpretation • Bioinformatics tools and software (e.g., R, Python) • Machine learning algorithms for DNA variant calling • Data visualization and scientific reporting Students will: 1. Learn about Breast Cancer Subtypes: Gain foundational knowledge about the biology of cancer and breast cancer subtypes (e.g., HER2-positive, triple-negative, hormone receptor-positive). 2. Explore Genome Sequencing: Understand the basics of genome sequencing technologies and the types of data generated. 3. Data Acquisition: Obtain publicly available cancer genome sequencing datasets from resources such as The Cancer Genome Atlas (TCGA). 4. Data Processing: Use bioinformatics tools to preprocess and clean the raw sequencing data. 5. Variant Calling: Apply machine learning algorithms to classify breast cancer subtypes based on the genetic data. 6. Visualization and Reporting: Create visualizations to represent findings and compile the results into a scientific research paper. Potential Student Outcomes • Scientific Research Paper: A detailed report outlining the methodology, analysis, and findings of the project. • Data Visualizations: Graphs and charts that illustrate key aspects of the data and the results of the machine learning models. • Presentation: A PowerPoint or poster presentation summarizing the project for academic or science fair settings. • Code Repository: A well-documented codebase for data processing and analysis, potentially shared on platforms like GitHub for community use. This project will provide students with hands-on experience in cancer genomics research, enhance their computational skills, and give them a taste of real-world applications of bioinformatics and machine learning in healthcare.