AIMC Topic: Transcriptome
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DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment.
Proceedings of the National Academy of Sciences of the United States of America
Nov 8, 2024
Cellular deconvolution via bulk RNA sequencing (RNA-seq) presents a cost-effective and efficient alternative to experimental methods such as flow cytometry and single-cell RNA-seq (scRNA-seq) for analyzing the complex cellular composition of tumor mi...
Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.
JCI insight
Nov 8, 2024
Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high di...
Comprehensive Analysis of Immune Infiltration and Key Genes in Peri-Implantitis Using Bioinformatics and Molecular Biology Approaches.
Medical science monitor : international medical journal of experimental and clinical research
Nov 6, 2024
BACKGROUND Peri-implantitis is the main cause of failure of implant treatment, and there is little research on its molecular mechanism. This study aimed to identify key biomarkers and immune infiltration of peri-implantitis using a bioinformatics met...
Screening of CAD-related secretory genes associated with type II diabetes based on comprehensive bioinformatics analysis and machine learning.
BMC cardiovascular disorders
Nov 5, 2024
BACKGROUND: Type II diabetes mellitus (T2DM) is strongly linked with a heightened risk of coronary artery disease (CAD). Exploring biological targets common to T2DM and CAD is essential for CAD intervention strategies.
Fibroblast Growth Factor 7
Diabetes Mellitus, Type 2
Single-Cell Analysis
Gene Regulatory Networks
Genetic Predisposition to Disease
Male
Coronary Artery Disease
Computational Biology
Middle Aged
Humans
Databases, Genetic
Myocytes, Cardiac
Gene Expression Profiling
Machine Learning
Myocytes, Smooth Muscle
Transcriptome
Female
Aged
Identification of novel markers for neuroblastoma immunoclustering using machine learning.
Frontiers in immunology
Nov 4, 2024
BACKGROUND: Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated,...
Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.
Cancer science
Nov 3, 2024
Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD),...
Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases.
Scientific reports
Nov 3, 2024
Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researc...
HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data.
Nature communications
Nov 2, 2024
In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in reality only a small fraction of them may actually be perturbed. Here, using simulations, we demonstr...
Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis.
International journal of biological macromolecules
Oct 31, 2024
In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functi...
Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches.
Frontiers in immunology
Oct 31, 2024
BACKGROUND: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved.
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