A group project on Neural Network Training and Differential Expression Analysis of age-related macular degeneration (ArMD).
This was a freeform final project idea showcasing biometric data analysis and ML capabilities. The project idea
was brought up from an interest in neuro-ophthalmology and the prevalence of degenerative eye diseases in today's aging population.
The project was divided into two parts; first, to train a neural network model to predict ArMD diagnosis based on
fundus images from a Ocular Disease Intelligent Recognition (ODIR) dataset, and second, to analyze differential gene
expression data from the Gene Expression Omnibus (GEO) database to identify potential biomarkers for ArMD diagnosis.
In collaboration with: Amiyo Das and Andy Park for UT Austin's Systems Biology course with Dr. Elif Cenik.
Made using: Python, TensorFlow, Keras, scikit-learn, pandas, numpy, Jupyter Notebook
Source code repository for ArMD Diagnostic Model
Central Carbon Metabolization
A block course project on carbon utilization between two carbon sources in CRP knockout E. coli.
An experiment was run by inoculating E. coli strain K12 in different monosaccharide sources:
D-Galactose, D-Xylose, and Acetate, as well as mixtures of two sugars. Their growth in the experimental phase
was documented through FIA-MS as a measure of OD over time. Mass spectrometry data was run in a Matlab workflow
designed for a differential analysis of all single and mixture carbon sources against D-Galactose alone. The hope
for the experiment was to find insights into carbon source utilization in knockout organisms, thereby determing
functions of the CRP gene in E. coli.
In collaboration with: Nando Flückiger under supervision of the Zamboni Lab with special thanks to
Prof. Dr. Nicola Zamboni and Dr. Mario Povoa Correia.
Done using: Matlab and FIA-MS
