AIMC Topic: Gene Expression Profiling
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Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 21, 2024
Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Exist...
scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data.
International journal of molecular sciences
Feb 13, 2024
Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations...
Deep learning-based quantification and transcriptomic profiling reveal a methyl jasmonate-mediated glandular trichome formation pathway in Cannabis sativa.
The Plant journal : for cell and molecular biology
Feb 8, 2024
Cannabis glandular trichomes (GTs) are economically and biotechnologically important structures that have a remarkable morphology and capacity to produce, store, and secrete diverse classes of secondary metabolites. However, our understanding of the ...
CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 2, 2024
Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficu...
Molecular Investigation and Preliminary Validation of Candidate Genes Associated with Neurological Damage in Heat Stroke.
Molecular neurobiology
Jan 31, 2024
Heat stroke (HS) is a severe medical condition characterized by a systemic inflammatory response that may precipitate multi-organ dysfunction, with a particular predilection for inducing profound central nervous system impairments. We aim to employ b...
Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.
Journal of chemical information and modeling
Jan 31, 2024
Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in o...
Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.
Nature communications
Jan 26, 2024
Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing...
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...
Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).
Oncology reports
Jan 19, 2024
Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has be...
DeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics.
Genome biology
Jan 19, 2024
Existing RNA velocity estimation methods strongly rely on predefined dynamics and cell-agnostic constant transcriptional kinetic rates, assumptions often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. Using a graph...
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