AIMC Topic: Transcriptome
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Exploring liquid-liquid phase separation-related diagnostic biomarkers in osteoarthritis based on machine learning algorithms and experiment.
Immunobiology
Jun 9, 2024
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
Jun 9, 2024
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...
Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing.
British journal of cancer
Jun 7, 2024
BACKGROUND: It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for ident...
Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.
Human genomics
Jun 5, 2024
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...
Signal Transduction
Neoplasm Recurrence, Local
Liver Transplantation
Protein Interaction Maps
Gene Regulatory Networks
Gene Expression Regulation, Neoplastic
Middle Aged
Gene Expression Profiling
Transcriptome
Deep Learning
Liver Neoplasms
Female
Carcinoma, Hepatocellular
Biomarkers, Tumor
Male
Humans
Machine learning and related approaches in transcriptomics.
Biochemical and biophysical research communications
Jun 4, 2024
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...
Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.
BMC musculoskeletal disorders
Jun 3, 2024
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
Jun 3, 2024
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 ...
Integrated analysis of multiple transcriptomic approaches and machine learning integration algorithms reveals high endothelial venules as a prognostic immune-related biomarker in bladder cancer.
International immunopharmacology
Jun 1, 2024
BACKGROUND: Despite the availability of established surgical and chemotherapy options, the treatment of bladder cancer (BCa) patients remains challenging. While immunotherapy has emerged as a promising approach, its benefits are limited to a subset o...
Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.
NPJ systems biology and applications
May 30, 2024
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually chang...
Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics.
International journal of molecular sciences
May 29, 2024
This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aim...
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