AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Gene Expression

Showing 11 to 20 of 183 articles

Clear Filters

Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data.

PloS one
The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans;...

Explainable artificial intelligence to predict and identify prostate cancer tissue by gene expression.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Prostate cancer is one of the most prevalent forms of cancer in men worldwide. Traditional screening strategies such as serum PSA levels, which are not necessarily cancer-specific, or digital rectal exams, which are often in...

Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data.

Scientific reports
The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of...

GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging.

Genome biology
When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a dee...

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Nature genetics
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the ful...

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image.

Medical image analysis
Inferring gene expressions from histopathological images has long been a fascinating yet challenging task, primarily due to the substantial disparities between the two modality. Existing strategies using local or global features of histological image...

N-of-one differential gene expression without control samples using a deep generative model.

Genome biology
Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can...

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi.

PLoS computational biology
mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly importan...

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.

Science advances
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, ar...

SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data.

Computer methods and programs in biomedicine
BACKGROUND: In Diffuse Large B-Cell Lymphoma (DLBCL), several methodologies are emerging to derive novel biomarkers to be incorporated in the risk assessment. We realized a pipeline that relies on autoencoders (AE) and Explainable Artificial Intellig...