AIMC Topic: Transcriptome

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DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

BMC bioinformatics Jul 4, 2023
BACKGROUND: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of ...
Bayes Theorem Deep Learning Machine Learning Humans Transcriptome Neoplasms
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Deep learning applications in single-cell genomics and transcriptomics data analysis.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie Jul 1, 2023
Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological system...
Machine Learning Deep Learning Genomics Transcriptome Gene Expression Profiling
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scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics.

Genome biology Jun 23, 2023
Despite the continued efforts, a batch-insensitive tool that can both infer and predict the developmental dynamics using single-cell genomics is lacking. Here, I present scTour, a novel deep learning architecture to perform robust inference and accur...
Gene Expression Profiling Transcriptome Genomics Deep Learning
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Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data.

Computers in biology and medicine Jun 12, 2023
BACKGROUND: Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis.
Endothelial Cells Head and Neck Neoplasms Ligands Cell Communication Sequence Analysis, RNA Squamous Cell Carcinoma of Head and Neck Deep Learning Melanoma Humans Transcriptome Tumor Microenvironment
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Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.

Archives of pharmacal research Jun 1, 2023
The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and...
Humans Transcriptome Mutation Nervous System Diseases Deep Learning
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SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.

Biomolecules May 27, 2023
Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The...
Transcriptome Gene Expression Profiling Deep Learning Artificial Intelligence
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TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes.

Chemical research in toxicology May 18, 2023
Animal studies are required for the evaluation of candidate drugs to ensure patient and volunteer safety. Toxicogenomics is often applied in these studies to gain understanding of the underlying mechanisms of toxicity, which is usually focused on cri...
Transcriptome Male Rats Female Animals Artificial Intelligence
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Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.

Nature biomedical engineering May 1, 2023
Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-dri...
Humans Gene Expression Profiling Transcriptome Machine Learning
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DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently ...
Mammals Transcriptome Deep Learning Adenosine Animals
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Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, i...
Deep Learning Supervised Machine Learning Transcriptome Neural Networks, Computer
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