AIMC Topic: Transcriptome
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Reliable interpretability of biology-inspired deep neural networks.
NPJ systems biology and applications
Oct 10, 2023
Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where re...
Prediction of Multiple Types of RNA Modifications via Biological Language Model.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2023
It has been demonstrated that RNA modifications play essential roles in multiple biological processes. Accurate identification of RNA modifications in the transcriptome is critical for providing insights into the biological functions and mechanisms. ...
Immune response and mesenchymal transition of papillary thyroid carcinoma reflected in ultrasonography features assessed by radiologists and deep learning.
Journal of advanced research
Oct 1, 2023
INTRODUCTION: Ultrasonography (US) features of papillary thyroid cancers (PTCs) are used to select nodules for biopsy due to their association with tumor behavior. However, the molecular biological mechanisms that lead to the characteristic US featur...
Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.
Scientific reports
Sep 23, 2023
Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars a...
STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering.
Computers in biology and medicine
Sep 9, 2023
BACKGROUND: Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morpholog...
Recent methodological advances towards single-cell proteomics.
Proceedings of the Japan Academy. Series B, Physical and biological sciences
Sep 6, 2023
Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and tran...
Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.
Scientific reports
Aug 21, 2023
Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, ...
De novo drug design based on patient gene expression profiles via deep learning.
Molecular informatics
Aug 21, 2023
Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candida...
Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture.
Medical & biological engineering & computing
Aug 2, 2023
Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data-based analyses are a...
Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.
Journal of cancer research and clinical oncology
Jul 28, 2023
BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adeno...
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