AIMC Topic: Deep Learning

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SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.

Bioinformatics (Oxford, England)
MOTIVATION: The rise of single-cell RNA sequencing (scRNA-seq) technology presents new opportunities for constructing detailed cell type-specific gene regulatory networks (GRNs) to study cell heterogeneity. However, challenges caused by noises, techn...

DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.

Bioinformatics (Oxford, England)
MOTIVATION: Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequenci...

Automated cooling tower detection through deep learning for Legionnaires' disease outbreak investigations: a model development and validation study.

The Lancet. Digital health
BACKGROUND: Cooling towers containing Legionella spp are a high-risk source of Legionnaires' disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be pr...

A deep learning method to predict bacterial ADP-ribosyltransferase toxins.

Bioinformatics (Oxford, England)
MOTIVATION: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent v...

Histological tissue classification with a novel statistical filter-based convolutional neural network.

Anatomia, histologia, embryologia
Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient a...

Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.

JNCI cancer spectrum
Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG...

Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.

Radiology. Artificial intelligence
Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Bre...

Improving Automated Hemorrhage Detection at Sparse-View CT via U-Net-based Artifact Reduction.

Radiology. Artificial intelligence
Purpose To explore the potential benefits of deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Materials and Methods In this retrospective study, a U-Net was trained for artifact ...