Tyler Rose

Earth and climate · Paper · June 2024

WaterGate: An Accessible Computational Model of Flooding Patterns

Navvye Anand, George Cheng, Tyler Rose

240 million people are affected by floods each year, reflecting the urgent need for accessible flood prediction and detection. WaterGate is a computational model that uses geographic elevation data and the rational method to predict flooding patterns, generating an interactive 3D model for user accessibility. Computational hydrology applies numerical methods, machine learning algorithms, and computational simulations to understand, predict, and manage water resources, including floods. Our project employs computational hydrology by analyzing the structure of river tributaries in 2D through polygon clustering, satellite imaging, and various cleaning protocols. We developed respective tributary tree graphs, morphological graphs, and nodes to create a comprehensive tree and 3D model. Afterward, we examine the morphology of flood plains in 3D space, implementing the rational method (Q = CiA) framework with curated relief plots to predict, model, and visualize flooding elevation. Then, we constructed our stream order analysis, waterline delineation, and statistical analysis to validate our data. Lastly, we modeled different river systems and developed further extensions to increase the applicability of WaterGate to communities around the world.

Biology and biomedicine · Paper · June 2024

PLAPT: Protein-Ligand Binding Affinity Prediction Using Pre-Trained Transformers

Tyler Rose, Nicolò Monti, Navvye Anand, Tianyu Shen

Predicting protein-ligand binding affinity is crucial for drug discovery, as it enables efficient identification of drug candidates. We introduce PLAPT, a novel model utilizing transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPT's superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.