Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

Abstract

The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.

Attention is all you need: utilizing attention in AI-enabled drug discovery

“…Firstly, BERT’s bidirectional nature enables it to capture nuanced information within molecular structures, including interactions among atoms and the molecule’s overall configuration. This capability offers a highly detailed representation for complex drug molecules [ 75–77 ]. Furthermore, BERT employs a pre-train/fine-tune paradigm, allowing it to be initially trained on large datasets and later fine-tuned for specific tasks.…”

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

“…This heat is then used to The transformer is the most novel and ubiquitous DL model, consisting of encoder and decoder blocks, which in turn contain self-attention modules that can handle various input sizes and effectively capture long-range dependencies. It currently dominates natural language processing (NLP) but is also utilized in various domains, such as computer vision, audio processing, and generative AI [47][48][49].…”

From beasts to bytes: Revolutionizing zoological research with artificial intelligence

“…Transformer-based models are fundamentally structured around the attention mechanism and its derived framework, multi-head attention ( Choi & Lee, 2023 ). Leveraging this core architecture, transformer-based models can overcome the limitations of RNN models, which cannot parallelize input processing across all time steps, while still effectively capturing the positional dependencies inherent in sequential data in tasks such as language translation, text summarization, image captioning, and speech recognition.…”

“…Leveraging this core architecture, transformer-based models can overcome the limitations of RNN models, which cannot parallelize input processing across all time steps, while still effectively capturing the positional dependencies inherent in sequential data in tasks such as language translation, text summarization, image captioning, and speech recognition. The transformer includes encoder and decoder structures for processing sequence inputs and generating corresponding outputs, respectively ( Choi & Lee, 2023 ). The architectural variant BERT, which only incorporates the encoder structure, employs random sequence masking during its pre-training tasks, leading to superior outcomes in protein 3D structure prediction ( Lin et al, 2022b ) and single-cell annotation ( Yang et al, 2022 ).…”