AIMC Topic: Gene Expression Profiling

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Robust classification using average correlations as features (ACF).

BMC bioinformatics Mar 20, 2023
MOTIVATION: In single-cell transcriptomics and other omics technologies, large fractions of missing values commonly occur. Researchers often either consider only those features that were measured for each instance of their dataset, thereby accepting ...
Cluster Analysis Computer Simulation Gene Expression Profiling Machine Learning Algorithms
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A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods.

Computational biology and chemistry Mar 5, 2023
The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 4...
Humans Female Breast Neoplasms Gene Expression Profiling Artificial Intelligence Algorithms
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Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.

Communications biology Feb 25, 2023
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. Th...
Sequence Analysis, RNA Microarray Analysis Machine Learning Gene Expression Profiling RNA-Seq
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Artificial intelligence and high-dimensional technologies in the theragnosis of systemic lupus erythematosus.

The Lancet. Rheumatology Feb 20, 2023
Systemic lupus erythematosus is a complex, systemic autoimmune disease characterised by immune dysregulation. Pathogenesis is multifactorial, contributing to clinical heterogeneity and posing challenges for diagnosis and treatment. Although strides i...
Epigenomics United States Gene Expression Profiling Humans Lupus Erythematosus, Systemic Artificial Intelligence Genomics
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Single-cell RNA-seq data analysis based on directed graph neural network.

Methods (San Diego, Calif.) Feb 16, 2023
Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and comple...
Neural Networks, Computer Data Analysis Single-Cell Gene Expression Analysis Sequence Analysis, RNA Cluster Analysis Gene Expression Profiling Single-Cell Analysis High-Throughput Nucleotide Sequencing
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Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics.

Contrast media & molecular imaging Feb 5, 2023
BACKGROUND: Deep learning techniques are gaining momentum in medical research. Colorectal adenoma (CRA) is a precancerous lesion that may develop into colorectal cancer (CRC) and its etiology and pathogenesis are unclear. This study aims to identify ...
Gene Regulatory Networks Gene Expression Profiling Humans MicroRNAs Gene Expression Regulation, Neoplastic Adenoma Colorectal Neoplasms Deep Learning Computational Biology
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LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications.

Gene Feb 1, 2023
OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secre...
Gene Expression Profiling Transcriptome Neural Networks, Computer Algorithms Humans
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DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.

Molecules (Basel, Switzerland) Jan 14, 2023
Synergistic drug combinations have demonstrated effective therapeutic effects in cancer treatment. Deep learning methods accelerate identification of novel drug combinations by reducing the search space. However, potential adverse drug-drug interacti...
Combined Modality Therapy Drug Combinations Drug Interactions Neural Networks, Computer Gene Expression Profiling
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Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.

Proceedings of the National Academy of Sciences of the United States of America Dec 28, 2022
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional...
Deep Learning Image Processing, Computer-Assisted Phenotype Robotics Gene Expression Profiling Robotic Surgical Procedures Transcriptome
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Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network.

Genome research Dec 16, 2022
Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assig...
Single-Cell Gene Expression Analysis Mice Sequence Analysis, RNA Genome Single-Cell Analysis Gene Regulatory Networks Humans Animals Gene Expression Profiling Neural Networks, Computer Biomarkers
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