AIMC Topic: Gene Expression Profiling

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An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks.

Scientific reports
Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus m...

Elucidating the role of KLRD1 in coronary atherosclerosis: harnessing bioinformatics and machine learning to advance understanding.

Journal of cardiothoracic surgery
BACKGROUND: Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major contributor to cardiovascular diseases. KLRD1 is a gene that encodes a protein involved...

Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.

Medicine
Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cancer (CRC). However, the mechanisms that sustain CSCs in CRC remain unclear. This study aimed to identi...

The role of mitochondrial dysfunction in the pathogenesis of atherosclerosis: A new exploration from bioinformatics analysis.

Medicine
Atherosclerosis (AS) is a complex cardiovascular disease associated with mitochondrial dysfunction (MD), which contributes to plaque formation and instability. This study explores the relationship and shared risk factors between the pathogenesis of A...

Machine learning identifies SRD5A3 as a propionate-related prognostic biomarker in triple-negative breast cancer.

Scientific reports
The increased risk of recurrence and metastasis are obstacles to treating TNBC. Propionate-related genes play an important role in tumor development and immune cell infiltration. The study was to identify the association between propionate-related ge...

scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links.

Nature communications
Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our u...

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets.

Scientific reports
Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcript...

Identifying shared hub genes in LIRI and MASLD through bioinformatics analysis and machine learning.

Scientific reports
Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) are more susceptible to liver ischemia-reperfusion injury (LIRI), complicating liver surgery outcomes. This study aimed to uncover shared hub genes and mechanisms linking ...

Integrating bioinformatics and machine learning to identify biomarkers of branched chain amino acid related genes in osteoarthritis.

BMC musculoskeletal disorders
BACKGROUND: Branched-chain amino acids (BCAA) metabolism is significantly associated with osteoarthritis (OA), but the specific mechanism of BCAA related genes (BCAA-RGs) in OA is still unclear. Therefore, this research intended to identify potential...

Prediction of Drug-Induced Nephrotoxicity Using Chemical Information and Transcriptomics Data.

Journal of chemical information and modeling
Prediction of drug-induced nephrotoxicity is an important task in the drug discovery and development pipeline. Chemical information-based machine learning models are used in general for nephrotoxicity prediction as a part of computational modeling. C...