A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

• Published in: IEEE ... International Conference on Trust, Security and Privacy in Computing and Communications (Online) pp. 2781 - 2788
• Main Authors: Tian, Cheng, Liu, Song, Li, Jinbao, Wang, Guangchen, Kong, Luyue
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2023
• Subjects: BERT Pre-training; Biological system modeling; Biometrics (access control); Feature extraction; Gene expression; Genetic Variation; Genomics; Predictive models; Privacy; Residual Attention Network; Transcriptome Prediction
• ISSN: 2324-9013
• DOI: 10.1109/TrustCom60117.2023.00388

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.