AIMC Topic: Transcriptome

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Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.

Scientific reports
Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored...

Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.

Scientific reports
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatments, underscoring the critical need for early detection and diagnosis to improve intervention outcomes. This study integrates various bioinformatics me...

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.

Computational biology and chemistry
Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenoca...

Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.

NAR genomics and bioinformatics
Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether...

Machine Learning Analysis of the Orbitofrontal Cortex Transcriptome of Human Opioid Users Identifies Shisa7 as a Translational Target Relevant for Heroin Seeking Leveraging a Male Rat Model.

Biological psychiatry
BACKGROUND: Identifying neurobiological targets predictive of the molecular neuropathophysiological signature of human opioid use disorder (OUD) could expedite new treatments. OUD is characterized by dysregulated cognition and goal-directed behavior ...

Integrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis: developing an individualized clinical prediction tool.

Experimental biology and medicine (Maywood, N.J.)
Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate prediction of disease progression challenging. This study aimed to develop molecular-level prognostic m...

Identification and experimental validation of diagnostic and prognostic genes CX3CR1, PID1 and PTGDS in sepsis and ARDS using bulk and single-cell transcriptomic analysis and machine learning.

Frontiers in immunology
BACKGROUND: Sepsis is an uncontrolled reaction to infection that causes severe organ dysfunction and is a primary cause of ARDS. Patients suffering both sepsis and ARDS have a poor prognosis and high mortality. However, the mechanisms behind their si...

Machine learning analysis of CD4+ T cell gene expression in diverse diseases: insights from cancer, metabolic, respiratory, and digestive disorders.

Cancer genetics
CD4 T cells play a pivotal role in the immune system, particularly in adaptive immunity, by orchestrating and enhancing immune responses. CD4 T cell-related immune responses exhibit diverse characteristics in different diseases. This study utilizes g...

Comparative transcriptomic and molecular biology analyses to explore potential immune responses to challenge in .

Frontiers in cellular and infection microbiology
is a significant pathogen affecting shrimp and crab farming, particularly strains carrying genes associated with acute hepatopancreatic necrosis syndrome. However, the immune response of to infection remains unclear. To address this knowledge gap,...

Spatially-resolved analyses of muscle invasive bladder cancer microenvironment unveil a distinct fibroblast cluster associated with prognosis.

Frontiers in immunology
BACKGROUND: Muscle-invasive bladder cancer (MIBC) is a prevalent cancer characterized by molecular and clinical heterogeneity. Assessing the spatial heterogeneity of the MIBC microenvironment is crucial to understand its clinical significance.