AI Medical Compendium Topic

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Genomics

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Deep learning applications in single-cell genomics and transcriptomics data analysis.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
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...

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Translational psychiatry
Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk f...

ExplaiNN: interpretable and transparent neural networks for genomics.

Genome biology
Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear models. ExplaiNN can predict TF b...

scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics.

Genome biology
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...

An automated framework for evaluation of deep learning models for splice site predictions.

Scientific reports
A novel framework for the automated evaluation of various deep learning-based splice site detectors is presented. The framework eliminates time-consuming development and experimenting activities for different codebases, architectures, and configurati...

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases.

Journal of biomedical informatics
BACKGROUND: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to im...

Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Mechanistic-based Model simulations (MM) are an effective approach commonly employed, for research and learning purposes, to better investigate and understand the inherent behavior of biological systems. Recent advancements ...

SENet: A deep learning framework for discriminating super- and typical enhancers by sequence information.

Computational biology and chemistry
Super-enhancers are large domains on the genome where multiple short typical enhancers within a specific genomic distance are stitched together. Typically, they are cell type-specific and responsible for defining cell identity and regulating gene tra...

Geometric graph neural networks on multi-omics data to predict cancer survival outcomes.

Computers in biology and medicine
The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis predictions and subsequent treatment strategies, it is imperative to develop advanced computational ...

DeepCGP: A Deep Learning Method to Compress Genome-Wide Polymorphisms for Predicting Phenotype of Rice.

IEEE/ACM transactions on computational biology and bioinformatics
Genomic selection (GS) is expected to accelerate plant and animal breeding. During the last decade, genome-wide polymorphism data have increased, which has raised concerns about storage cost and computational time. Several individual studies have att...