AIMC Topic: Transcriptome

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Novel gene signatures predicting and immune infiltration analysis in Parkinson's disease: based on combining random forest with artificial neural network.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology Jan 24, 2024
BACKGROUND: Parkinson's disease (PD) ranks as the second most prevalent neurodegenerative disorder globally, and its incidence is rapidly rising. The diagnosis of PD relies on clinical characteristics. Although current treatments aim to alleviate sym...
Neural Networks, Computer Machine Learning Gene Expression Profiling Humans Parkinson Disease Mast Cells Random Forest Transcriptome Computational Biology
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Time-Course Transcriptome Analysis Reveals Distinct Phases and Identifies Two Key Genes during Severe Fever with Thrombocytopenia Syndrome Virus Infection in PMA-Induced THP-1 Cells.

Viruses Dec 29, 2023
In recent years, there have been significant advancements in the research of Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV). However, several limitations and challenges still exist. For instance, researchers face constraints regarding expe...
Humans Gene Expression Profiling THP-1 Cells Cluster Analysis Transcriptome Severe Fever with Thrombocytopenia Syndrome
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Identifying multi-target drugs for prostate cancer using machine learning-assisted transcriptomic analysis.

Journal of biomolecular structure & dynamics Dec 16, 2023
Prostate cancer is a leading cause of cancer death in men, and the development of effective treatments is of great importance. This study explored to identify the candidate drugs for prostate cancer by transcriptomic data and CMap database analysis. ...
Antineoplastic Agents Gene Expression Profiling Transcriptome Male Drug Discovery Cell Proliferation Prostatic Neoplasms Bisoprolol Cell Line, Tumor Gene Expression Regulation, Neoplastic Humans Machine Learning
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Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting.

Cell systems Dec 12, 2023
Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplet...
RNA, Guide, CRISPR-Cas Systems Humans CRISPR-Cas Systems Deep Learning RNA Transcriptome
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Generating bulk RNA-Seq gene expression data based on generative deep learning models and utilizing it for data augmentation.

Computers in biology and medicine Dec 7, 2023
Large-scale high-throughput transcriptome sequencing data holds significant value in biomedical research. However, practical challenges such as difficulty in sample acquisition often limit the availability of large sample sizes, leading to decreased ...
Deep Learning RNA-Seq High-Throughput Nucleotide Sequencing Reproducibility of Results Transcriptome
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Personal transcriptome variation is poorly explained by current genomic deep learning models.

Nature genetics Nov 30, 2023
Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the refer...
Transcriptome Deep Learning Humans Genome Genomics Genetic Variation
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Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning.

Biochemical genetics Nov 22, 2023
The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coin...
Tuberculosis Computational Biology Coinfection COVID-19 SARS-CoV-2 Humans Gene Expression Profiling Machine Learning Transcriptome Protein Interaction Maps
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SPACEL: deep learning-based characterization of spatial transcriptome architectures.

Nature communications Nov 22, 2023
Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, j...
Transcriptome Deep Learning Algorithms Models, Statistical Gene Expression Profiling
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Associations of maternal stress, gene expression, and newborn birthweight in the Democratic Republic of Congo.

American journal of biological anthropology Oct 27, 2023
OBJECTIVES: Maternal stress has long been associated with lower birthweight, which is associated with adverse health outcomes including many adult diseases. The underlying mechanisms remain elusive although changes in gene expression may play a role....
Placenta Adult Gene Expression Transcriptome Democratic Republic of the Congo Stress, Psychological Humans Infant, Newborn Birth Weight Pregnancy Young Adult Female
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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 ...
Support Vector Machine Cluster Analysis Single-Cell Analysis Single-Cell Gene Expression Analysis Sequence Analysis, RNA Machine Learning Gene Expression Profiling Algorithms Transcriptome
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