Home // BIOTECHNO 2023, The Fifteenth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies // View article

A Microservice-oriented AI Automation Framework for Supporting Single-cell Downstream Analysis

Authors:
Hong Qing Yu
Ali Kermanizadeh
Sam O'Neill
Oyetola Florence Idowu

Keywords: Single-cell analysis; RNA-Seq; Machine Learning; AI Automation; Downstream Analysis; Knowledge Graph

Abstract:
Single-cell analysis has real potential for reshaping the future of biomedical research allowing for a better understanding of the natural properties of both healthy and diseased tissues that, in turn, allow for better opportunities for overcoming current challenges in drug discovery, diagnostics and prognostics. A large body of research in this field produces large quantities of data. Merging with fast-developed Machine learning (ML) and Artificial Intelligent (AI) algorithms would allow single-cell analysis research to be conducted more efficiently and accurately than currently possible. Therefore, there has been a surge of ML and AI developments for the whole life cycle of the downstream single-cell analysis process. However, there is a limitation to reusing, exchanging, sharing, applying the most advanced technologies, and automating the experimental environments and outcomes in cross-disciplinary collaborative research. This paper presents an automation framework to address these limitations and shows how AI and ML research can contribute to biomedical automation and control. Moreover, the real-world case will be evaluated to demonstrate the prototype implementation at the end of the paper.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2023

Publication date: March 13, 2023

Published in: conference

ISSN: 2308-4383

ISBN: 978-1-68558-058-2

Location: Barcelona, Spain

Dates: from March 13, 2023 to March 17, 2023