AIMC Topic: Gene Expression Profiling

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Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning.

PloS one
Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with th...

Exploring liquid-liquid phase separation-related diagnostic biomarkers in osteoarthritis based on machine learning algorithms and experiment.

Immunobiology
BACKGROUND: Osteoarthritis (OA) is a prevalent joint disorder characterized by cartilage degeneration and joint inflammation. Liquid-liquid phase separation (LLPS), a biophysical process involved in cellular organization, has recently gained attentio...

Accurate Identification of Spatial Domain by Incorporating Global Spatial Proximity and Local Expression Proximity.

Biomolecules
Accurate identification of spatial domains is essential in the analysis of spatial transcriptomics data in order to elucidate tissue microenvironments and biological functions. However, existing methods only perform domain segmentation based on local...

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

Human genomics
BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with rec...

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma.

Scientific reports
Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been ide...

Machine learning and related approaches in transcriptomics.

Biochemical and biophysical research communications
Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a s...

Thrombomodulin as a potential diagnostic marker of acute myocardial infarction and correlation with immune infiltration: Comprehensive analysis based on multiple machine learning.

Transplant immunology
BACKGROUND: Acute myocardial infarction (AMI) is a global health problem with high mortality. Early diagnosis can prevent the development of AMI and provide valuable information for subsequent treatment. Angiogenesis has been shown to be a critical f...

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals
BACKGROUND: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted th...

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BMC musculoskeletal disorders
BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both...

Prediction of Interactomic HUB Genes in Periodontitis With Acute Myocardial Infarction.

The Journal of craniofacial surgery
BACKGROUND: Acute myocardial infarction (AMI) risk correlates with C-reactive protein (CRP) levels, suggesting systemic inflammation is present well before AMI. Studying different types of periodontal disease (PD), extremely common in individuals at ...