AIMC Topic: Gene Expression Profiling

Clear Filters Showing 1511 to 1520 of 1601 articles

scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA-seq (scRNA-seq) has been widely used to resolve cellular heterogeneity. After collecting scRNA-seq data, the natural next step is to integrate the accumulated data to achieve a common ontology of cell types and states. Thu...

Identification of Potential Drug Therapy for Dermatofibrosarcoma Protuberans with Bioinformatics and Deep Learning Technology.

Current computer-aided drug design
BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a rare mesenchymal tumor that is primarily treated with surgery. Targeted therapy is a promising approach to help reduce the high rate of recurrence. This study aims to identify the potential targ...

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.

Bioscience reports
BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patien...

Representation learning of RNA velocity reveals robust cell transitions.

Proceedings of the National Academy of Sciences of the United States of America
RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-d...

A convolutional neural network for common coordinate registration of high-resolution histology images.

Bioinformatics (Oxford, England)
MOTIVATION: Registration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform 'common coordinate registration', akin to segmentation, in which samples are partitioned...

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.

Briefings in bioinformatics
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This 'black box' problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we pr...

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Briefings in bioinformatics
Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of...

Prediction of tumor purity from gene expression data using machine learning.

Briefings in bioinformatics
MOTIVATION: Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biologi...

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions.

Plant physiology
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance ...

Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease.

Rejuvenation research
In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of gen...