AI Medical Compendium Topic

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

Gene Expression

Showing 141 to 150 of 183 articles

Clear Filters

Multimodal probabilistic generative models for time-course gene expression data and Gene Ontology (GO) tags.

Mathematical biosciences
We propose four probabilistic generative models for simultaneously modeling gene expression levels and Gene Ontology (GO) tags. Unlike previous approaches for using GO tags, the joint modeling framework allows the two sources of information to comple...

Bioinformatics, interaction network analysis, and neural networks to characterize gene expression of radicular cyst and periapical granuloma.

Journal of endodontics
INTRODUCTION: Bioinformatics has emerged as an important tool to analyze the large amount of data generated by research in different diseases. In this study, gene expression for radicular cysts (RCs) and periapical granulomas (PGs) was characterized ...

Molecular classification of amyotrophic lateral sclerosis by unsupervised clustering of gene expression in motor cortex.

Neurobiology of disease
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive and ultimately fatal neurodegenerative disease, caused by the loss of motor neurons in the brain and spinal cord. Although 10% of ALS cases are familial (FALS), the majority are sporadic (S...

Machine learning ranking of plausible (un)explored synergistic gene combinations using sensitivity indices of time series measurements of Wnt signaling pathway.

Integrative biology : quantitative biosciences from nano to macro
Combinations of genes or proteins work in synergy at different times and durations in a signaling pathway. However, which combinations are prevalent at a particular time point or duration is mostly not known. Sensitivity analysis plays a major role i...

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Briefings in bioinformatics
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, lead...

An effective deep learning-based approach for splice site identification in gene expression.

Science progress
A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding introns disrupt e...

Identification of significant gene expression changes in multiple perturbation experiments using knockoffs.

Briefings in bioinformatics
Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are...

Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling.

Water science and technology : a journal of the International Association on Water Pollution Research
It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an incre...

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Briefings in bioinformatics
Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-...