AIMC Topic: Gene Expression Profiling

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Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation.

Scientific reports
Novel methods for detecting transplant rejection are craved, since conventional methods can detect ongoing rejection that may sometimes have already caused irreversible damage in transplanted organs. Here, we applied a transcriptomics database of rec...

Identification of prognostic genes related to T cell proliferation in papillary thyroid cancer based on single-cell RNA sequencing and bulk RNA sequencing data.

Clinical and experimental medicine
Papillary thyroid carcinoma (PTC) is the main pathological subtype of thyroid cancer. Given the strong association between T cells and PTC, this study focused on the prognostic value and potential molecular mechanisms of T cell proliferation-related ...

Anemia prediction using gene expression programming (GEP) and explainable artificial intelligence approaches.

Computers in biology and medicine
Anemia being a global health disorder, affecting millions of people, especially pregnant women, children, and the elderly. Proper and timely diagnosis must be ensured to prevent its adverse effects, but the traditional diagnostic methods are very tim...

CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.

BMC bioinformatics
BACKGROUND: Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.

Identification of DNA damage response and crotonylation-related biomarkers for lung adenocarcinoma via machine learning and WGCNA.

Clinical and experimental medicine
DNA damage response (DDR) and crotonylation occur frequently in lung adenocarcinoma (LUAD), but their relationship is yet to be elucidated. RNA sequencing data from LUAD patients in GSE27262 and GSE140797 datasets were obtained. DDR-crotonylation-rel...

SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics.

Genome biology
Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introdu...

Integrative transcriptomics and metabolomics reveal neuroendocrine-lipid crosstalk and adenosine signaling in broiler under heat stress.

BMC genomics
BACKGROUND: Heat stress (HS) is a significant challenge in poultry, negatively impacting feed efficiency and survival. These adaptive responses could lead to disrupted lipid metabolism, impaired immunity, and neural damage. We hypothesized that the n...

Interpretable graph Kolmogorov-Arnold networks for multi-cancer classification and biomarker identification using multi-omics data.

Scientific reports
The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics Graph Kolmogorov-Arnold Network...

SPP1 + macrophages facilitate pancreatic cancer progression via ITGB6-mediated interactions: evidence from integrated multi-omics analysis and experimental validation.

Immunologic research
Basement membranes (BMs) and tumor-associated macrophages (TAMs) are crucial stromal components in pancreatic cancer (PC), critically influencing disease progression. Bulk and single-cell RNA-seq (scRNA-seq) data were acquired from publicly available...

Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells.

European journal of medical research
BACKGROUND: Cardioembolic stroke (CS) and atherosclerosis (AS) are closely related diseases. Ferroptosis, a novel form of programmed cell death, may play a key role in CS and AS. However, the pathophysiological mechanisms underlying their coexistence...