Alexander Sewall Ford

Title: Innovations of Alexander Sewall Ford

Introduction

Alexander Sewall Ford is an accomplished inventor based in Seattle, WA (US). He has made significant contributions to the field of biotechnology, particularly in the prediction of enzyme interactions and protein sequences. With a total of 2 patents, Ford's work is at the forefront of machine learning applications in biological research.

Latest Patents

Ford's latest patents include groundbreaking techniques for predicting pairs of enzyme primary sequences and substrates, as well as their interaction probabilities. One exemplary method involves receiving a request to predict a pair of an enzyme primary sequence and a substrate. The process combines an enzyme vector, a substrate vector, and an interaction indication to form a machine learning model input. By applying a machine learning model to this input, Ford's method predicts the pair and outputs the interaction probability.

Another notable patent focuses on predicting protein sequences and structures. This method includes receiving a request to predict a missing area of a protein's primary sequence and its corresponding three-dimensional position. By utilizing various machine learning models, such as attention-based models and convolutional neural networks, Ford's approach effectively predicts the missing areas and their spatial configurations.

Career Highlights

Ford is currently employed at Amazon Technologies, Inc., where he continues to innovate and develop new technologies. His work has implications for various applications in biotechnology and pharmaceuticals, enhancing our understanding of protein interactions and functions.

Collaborations

Ford collaborates with talented individuals in his field, including Layne Christopher Price and Franziska Seeger. These partnerships foster a creative environment that drives innovation and leads to significant advancements in their research.

Conclusion

Alexander Sewall Ford is a prominent inventor whose work in predicting enzyme interactions and protein sequences is paving the way for future advancements in biotechnology. His contributions are vital to the ongoing evolution of machine learning applications in biological sciences.