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Updated: Jul 21, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Topological deep learning based deep mutational scanning.

Jiahui Chen¹, Daniel R Woldring², Faqing Huang³

¹Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR 72701, USA.

Computers in Biology and Medicine

|July 28, 2023

Summary

This summary is machine-generated.

We introduce a novel computational method, topological deep learning (TDL), to perform in silico deep mutational scanning (DMS). This approach accurately predicts protein binding interface mutations, aiding in drug discovery and vaccine design.

Keywords:

Antibody-resistance Deep mutational scanning Infectivity SARS-coV-2 Topological deep learning

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Area of Science:

Computational Biology
Protein Engineering
Immunology

Background:

High-throughput deep mutational scanning (DMS) is crucial for understanding protein function but faces limitations due to the vast mutational space.
Current experimental methods struggle to cover the entire mutational landscape of proteins.

Purpose of the Study:

To develop an in silico method for deep mutational scanning (DMS) using topological deep learning (TDL).
To address the limitations of experimental DMS by exploring the vast protein mutational space computationally.
To validate the TDL-DMS model for applications in protein engineering and infectious disease research.

Main Methods:

Developed a topological deep learning (TDL) paradigm for in silico DMS.
Employed a persistent spectral theory (persistent Laplacian) based topological data analysis (TDA) technique.
Captured topological invariants and homotopic shape evolution of data for mutation analysis.

Main Results:

Validated the TDL-DMS model using SARS-CoV-2 datasets.
Demonstrated excellent accuracy and reliability in predicting binding interface mutations.
Showcased the model's potential for SARS-CoV-2 variant forecasting and antibody design.

Conclusions:

The TDL-DMS model offers a powerful computational alternative to experimental DMS.
This approach has significant implications for accelerating drug discovery, vaccine design, and protein engineering.
The findings are vital for advancing precision medicine and understanding viral evolution.