ReviewBiology2023
Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.
Review in Biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
80 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Which chemical features are captured by ChemBERTa's attention?Journal of computer-aided molecular design · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- IRCAS: a novel end-to-end approach to identify, rectify, and classify comprehensive alternative splicing events in a transcriptome without genome reference.Briefings in bioinformatics · 2026Article
- Transformer-Type Architecture for Predicting Physicochemical Properties of Cosmetic Emulsions.ACS omega · 2026Article
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
- Multimodal Deep Learning with Attention-Based Fusion for Skin Cancer Diagnosis.Bioengineering (Basel, Switzerland) · 2026Article
- DS-transformer: a dual-stream transformer for lithium-ion battery state-of-health estimation via cross-attention fusion of discharge curves and impedance features.Scientific reports · 2026Article
- Transformers for single-cell RNA sequencing: a survey.Briefings in bioinformatics · 2026Review
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
- Leveraging weighted embedding and Transformer architecture to improve phenotype prediction of complex traits for crops.Nature communications · 2026Article
- Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions.Health science reports · 2026Review
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- Precision Biomarker Identification in Gynecological Cancers Using Coexpression Networks and Attention-Based LSTM in Healthcare 4.0.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation.Journal of imaging informatics in medicine · 2026Article
- Transformer Models, Graph Networks, and Generative AI in Gut Microbiome Research: A Narrative Review.Bioengineering (Basel, Switzerland) · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- A multi-branch network for cooperative spectrum sensing via attention-based and CNN feature fusion.Scientific reports · 2026Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.