AIMC Topic: Gene Expression Profiling
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XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.
PLoS computational biology
Oct 2, 2023
Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the...
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...
Methods for cell-type annotation on scRNA-seq data: A recent overview.
Journal of bioinformatics and computational biology
Sep 23, 2023
The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in sin...
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...
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, ...
The seductive allure of spatial biology: accelerating new discoveries in the life sciences.
Immunology and cell biology
Aug 12, 2023
Spatial biology is a rapidly developing field which enables the visualization of protein and transcriptomic data while preserving tissue context and architecture. Initially used in discovery, there is growing promise for translational and diagnostic ...
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...
Screening of the shared pathogenic genes of ulcerative colitis and colorectal cancer by integrated bioinformatics analysis.
Journal of cellular and molecular medicine
Jul 26, 2023
Ulcerative colitis (UC) is one of the high-risk pathogenic factors for colorectal cancer (CRC). However, the shared gene and signalling mechanisms between UC and CRC remain unclear. The goal of this study was to delve more into the probable causal re...
Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity.
Computers in biology and medicine
Jul 18, 2023
Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell a...
Deep learning of 2D-Restructured gene expression representations for improved low-sample therapeutic response prediction.
Computers in biology and medicine
Jul 18, 2023
Clinical outcome prediction is important for stratified therapeutics. Machine learning (ML) and deep learning (DL) methods facilitate therapeutic response prediction from transcriptomic profiles of cells and clinical samples. Clinical transcriptomic ...
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