AIMC Topic: Gene Expression Profiling

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Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.

Science advances Nov 8, 2023
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, ar...
Escherichia coli Proteins Gene Expression Profiling Deep Learning Gene Expression High-Throughput Nucleotide Sequencing Escherichia coli
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Maximum margin and global criterion based-recursive feature selection.

Neural networks : the official journal of the International Neural Network Society Nov 2, 2023
In this research paper, we aim to investigate and address the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. We identify two main challenges associated with these methods. Firstly, the...
Gene Expression Profiling Support Vector Machine Algorithms
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Identification of Potential Neddylation-related Key Genes in Ischemic Stroke based on Machine Learning Methods.

Molecular neurobiology Nov 1, 2023
Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even death. Understanding the molecular mechanisms involved in the occurrence and progression of IS is of great significance for developing effective treatme...
Machine Learning Gene Expression Profiling Humans Ischemic Stroke Gene Ontology NEDD8 Protein Gene Regulatory Networks Databases, Genetic
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On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.

PloS one Oct 19, 2023
Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains ...
Cluster Analysis Single-Cell Analysis Gene Expression Profiling Machine Learning Single-Cell Gene Expression Analysis Sequence Analysis, RNA Algorithms Transcriptome Support Vector Machine
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Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science.

IEEE/ACM transactions on computational biology and bioinformatics Oct 9, 2023
Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However,...
Cluster Analysis RNA Single-Cell Analysis Sequence Analysis, RNA Artificial Intelligence 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...
Databases, Factual Neoplasms Gene Expression Profiling Humans Artificial Intelligence Genes, Essential
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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...
Epithelial-Mesenchymal Transition Thyroid Cancer, Papillary Gene Expression Regulation, Neoplastic Radiologists Transcriptome Deep Learning Thyroid Neoplasms Ultrasonography Female Middle Aged Aged Gene Expression Profiling Humans Adult Male
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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...
Single-Cell Gene Expression Analysis Sequence Analysis, RNA Neural Networks, Computer Gene Expression Profiling
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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...
Algorithms Breast Neoplasms Transcriptome Cluster Analysis Gene Expression Profiling Humans Neural Networks, Computer
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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, ...
Gene Expression Profiling Transcriptome Animals Mammary Neoplasms, Animal Deep Learning Electric Power Supplies
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