AIMC Topic: RNA-Seq

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SCMAG: A Semisupervised Single-Cell Clustering Method Based on Matrix Aggregation Graph Convolutional Neural Network.

Computational and mathematical methods in medicine Oct 4, 2021
Clustering analysis is one of the most important technologies for single-cell data mining. It is widely used in the division of different gene sequences, the identification of functional genes, and the detection of new cell types. Although the tradit...
Supervised Machine Learning Humans Data Mining Databases, Nucleic Acid Neural Networks, Computer Single-Cell Analysis Algorithms RNA-Seq Computational Biology Cluster Analysis
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VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics.

Nature communications Sep 28, 2021
Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological ins...
Models, Genetic Datasets as Topic Single-Cell Analysis RNA-Seq Gene Regulatory Networks Mice Humans Animals Deep Learning
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EditPredict: Prediction of RNA editable sites with convolutional neural network.

Genomics Sep 23, 2021
RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to pr...
Neural Networks, Computer Animals RNA-Seq RNA Genome RNA Editing
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Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies.

Nature communications Sep 10, 2021
Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we...
Aged, 80 and over Animals Male Deep Learning Humans Brain Female Aged Alzheimer Disease Sex Factors Datasets as Topic Gene Expression Regulation Cohort Studies RNA-Seq Mice Microglia
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Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning.

BMC cancer Aug 27, 2021
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, ...
Algorithms Humans Neural Networks, Computer Biomarkers, Tumor RNA-Seq Machine Learning Liver Neoplasms Bayes Theorem Carcinoma, Hepatocellular
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Bidirectional deep neural networks to integrate RNA and DNA data for predicting outcome for patients with hepatocellular carcinoma.

Future oncology (London, England) Aug 10, 2021
Individualized patient profiling is instrumental for personalized management in hepatocellular carcinoma (HCC). This study built a model based on bidirectional deep neural networks (BiDNNs), an unsupervised machine-learning approach, to integrate mu...
Liver Neoplasms Carcinoma, Hepatocellular RNA-Seq Gene Regulatory Networks Middle Aged Risk Assessment Deep Learning Cohort Studies DNA Methylation Liver Neoplasm Recurrence, Local Disease-Free Survival RNA, Messenger Epigenesis, Genetic Gene Expression Regulation, Neoplastic Models, Genetic Humans Prognosis Female Aged Adult Biomarkers, Tumor Male Datasets as Topic
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SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells.

Genome biology Aug 10, 2021
Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3' tag-based scRNA-seq....
Polyadenylation Humans Deep Learning COVID-19 Single-Cell Analysis SARS-CoV-2 RNA-Seq Sensitivity and Specificity Sequence Analysis, RNA Transcriptome
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Identification of Tissue of Origin and Guided Therapeutic Applications in Cancers of Unknown Primary Using Deep Learning and RNA Sequencing (TransCUPtomics).

The Journal of molecular diagnostics : JMD Jul 26, 2021
Cancers of unknown primary (CUP) are metastatic cancers for which the primary tumor is not found despite thorough diagnostic investigations. Multiple molecular assays have been proposed to identify the tissue of origin (TOO) and inform clinical care;...
Humans Middle Aged Prospective Studies Transcriptome Retrospective Studies Aged, 80 and over Data Accuracy Deep Learning Female RNA-Seq Neoplasms, Unknown Primary Adult Aged Gene Fusion Biomarkers, Tumor Male
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Benchmarking and Testing Machine Learning Approaches with BARRA:CuRDa, a for Cancer Research.

Journal of computational biology : a journal of computational molecular cell biology Jul 14, 2021
Artificial Intelligence Machine Learning Benchmarking Humans RNA-Seq Algorithms Databases, Nucleic Acid Sequence Analysis, RNA Neoplasms Data Visualization Principal Component Analysis
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An active learning approach for clustering single-cell RNA-seq data.

Laboratory investigation; a journal of technical methods and pathology Jul 9, 2021
Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover...
Algorithms Cells, Cultured Reproducibility of Results Animals Leukocytes, Mononuclear Gene Expression Profiling Single-Cell Analysis RNA-Seq Humans Urinary Bladder Kidney Cluster Analysis Unsupervised Machine Learning Neurons
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