Transformer architecture and attention mechanisms in genome data analysis: a comprehensive review
SR Choi, M Lee - Biology, 2023 - mdpi.com
Simple Summary The rapidly advancing field of deep learning, specifically transformer-
based architectures and attention mechanisms, has found substantial applicability in …
based architectures and attention mechanisms, has found substantial applicability in …
Recent advances in deep learning for protein-protein interaction analysis: A comprehensive review
M Lee - Molecules, 2023 - mdpi.com
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative
imprint across multiple disciplines. Within computational biology, it is expediting progress in …
imprint across multiple disciplines. Within computational biology, it is expediting progress in …
Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience
Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and
evolution. In the last decade, many essential genes (EGs) have been determined using …
evolution. In the last decade, many essential genes (EGs) have been determined using …
'Bingo'—a large language model-and graph neural network-based workflow for the prediction of essential genes from protein data
The identification and characterization of essential genes are central to our understanding of
the core biological functions in eukaryotic organisms, and has important implications for the …
the core biological functions in eukaryotic organisms, and has important implications for the …
Decoding electromyographic signal with multiple labels for hand gesture recognition
Surface electromyography (sEMG) is a significant interaction signal in the fields of human-
computer interaction and rehabilitation assessment, as it can be used for hand gesture …
computer interaction and rehabilitation assessment, as it can be used for hand gesture …
A biologically interpretable graph convolutional network to link genetic risk pathways and neuroimaging markers of disease
We propose a novel end-to-end framework for whole-brain and whole-genome imaging-
genetics. Our genetics network uses hierarchical graph convolution and pooling operations …
genetics. Our genetics network uses hierarchical graph convolution and pooling operations …
omicsGAT: Graph attention network for cancer subtype analyses
The use of high-throughput omics technologies is becoming increasingly popular in all
facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative …
facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative …
Multi-modal fusion for robust hand gesture recognition based on heterogeneous networks
Hand gesture recognition has become a vital subject in the fields of human-computer
interaction and rehabilitation assessment. This paper presents a multi-modal fusion for hand …
interaction and rehabilitation assessment. This paper presents a multi-modal fusion for hand …
Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks
Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced
understanding of the spatial organization of gene expression within tumor tissues …
understanding of the spatial organization of gene expression within tumor tissues …
A protein network refinement method based on module discovery and biological information
L Pan, H Wang, B Yang, W Li - BMC bioinformatics, 2024 - Springer
Background The identification of essential proteins can help in understanding the minimum
requirements for cell survival and development to discover drug targets and prevent …
requirements for cell survival and development to discover drug targets and prevent …