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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Published on: December 15, 2023

Patient privacy in AI-driven omics methods.

Juexiao Zhou¹, Chao Huang², Xin Gao¹

¹Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.

Trends in Genetics : TIG

|April 18, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) in omics analysis poses privacy risks. This study outlines threats in data sharing and model training, and presents methods to protect patient privacy in AI-driven omics.

Keywords:

artificial intelligence omics privacy

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Area of Science:

Bioinformatics
Computational Biology
Artificial Intelligence

Background:

Omics data analysis is increasingly powered by artificial intelligence (AI).
AI applications in omics raise significant patient privacy concerns.
Protecting sensitive patient information is crucial for ethical research.

Purpose of the Study:

To identify key privacy risks associated with AI in omics analysis.
To discuss methods for safeguarding patient privacy throughout the AI lifecycle.
To propose ways to evaluate the effectiveness of privacy-preserving techniques.

Main Methods:

Review of risk factors in omics data sharing.
Analysis of privacy vulnerabilities during AI model training.
Examination of privacy challenges in releasing AI models and results.
Discussion of privacy safeguarding and evaluation strategies.

Main Results:

Identified specific threats to patient privacy in omics data sharing, AI model training, and model release.
Outlined various methods to mitigate these privacy risks.
Highlighted the importance of robust privacy evaluation metrics.

Conclusions:

AI in omics analysis necessitates proactive privacy protection measures.
Implementing safeguarding methods is essential for maintaining patient trust.
Continuous evaluation of privacy is critical for responsible AI deployment in genomics.