AIMC Topic: Transcriptome
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Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.
Cell reports. Medicine
Apr 26, 2024
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of mach...
Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.
Journal of molecular neuroscience : MN
Apr 25, 2024
We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCG...
Unveiling the link between lactate metabolism and rheumatoid arthritis through integration of bioinformatics and machine learning.
Scientific reports
Apr 22, 2024
Rheumatoid arthritis (RA) is a persistent autoimmune condition characterized by synovitis and joint damage. Recent findings suggest a potential link to abnormal lactate metabolism. This study aims to identify lactate metabolism-related genes (LMRGs) ...
Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.
Frontiers in immunology
Apr 22, 2024
BACKGROUND: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity...
Identification and validation of potential diagnostic signature and immune cell infiltration for HIRI based on cuproptosis-related genes through bioinformatics analysis and machine learning.
Frontiers in immunology
Apr 16, 2024
BACKGROUND AND AIMS: Cuproptosis has emerged as a significant contributor in the progression of various diseases. This study aimed to assess the potential impact of cuproptosis-related genes (CRGs) on the development of hepatic ischemia and reperfusi...
Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms.
Frontiers in immunology
Apr 15, 2024
BACKGROUND: In the face of continued growth in the elderly population, the need to understand and combat age-related cardiac decline becomes even more urgent, requiring us to uncover new pathological and cardioprotective pathways.
Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.
International journal of molecular sciences
Apr 11, 2024
Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, su...
Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators.
Cell metabolism
Apr 10, 2024
Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately ident...
Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis.
Computers in biology and medicine
Apr 9, 2024
Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly spec...
A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.
International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), ...
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