AIMC Topic: Transcriptome
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Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.
International journal of molecular sciences
Nov 19, 2024
In oncology, there is a critical need for robust biomarkers that can be easily translated into the clinic. We introduce a novel approach using paired differential gene expression analysis for biological feature selection in machine learning models, e...
A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer's Disease.
Cells
Nov 19, 2024
The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been develo...
SDC4 protein action and related key genes in nonhealing diabetic foot ulcers based on bioinformatics analysis and machine learning.
International journal of biological macromolecules
Nov 17, 2024
Diabetic foot ulcers (DFU) is a complication associated with diabetes characterised by high morbidity, disability, and mortality, involving chronic inflammation and infiltration of multiple immune cells. We aimed to identify the critical genes in non...
Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing.
Nature methods
Nov 14, 2024
The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process ...
Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers.
Molecular medicine (Cambridge, Mass.)
Nov 14, 2024
BACKGROUND: To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU).
SpaInGNN: Enhanced clustering and integration of spatial transcriptomics based on refined graph neural networks.
Methods (San Diego, Calif.)
Nov 13, 2024
Recent developments in spatial transcriptomics (ST) technology have markedly enhanced the proposed capacity to comprehensively characterize gene expression patterns within tissue microenvironments while crucially preserving spatial context. However, ...
Ferroptosis-related biomarkers for adamantinomatous craniopharyngioma treatment: conclusions from machine learning techniques.
Frontiers in endocrinology
Nov 13, 2024
INTRODUCTION: Adamantinomatous craniopharyngioma (ACP) is difficult to cure completely and prone to recurrence after surgery. Ferroptosis as an iron-dependent programmed cell death, may be a critical process in ACP. The study aimed to screen diagnost...
Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis.
Autoimmunity
Nov 12, 2024
Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship between key genes and immune infiltration. Differentially expressed genes (DEGs) were first screened by d...
Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response.
Genes
Nov 12, 2024
Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of breast cancer patients warrants further study. The anoikis-related prognostic signature (ANRS) was gene...
Imatinib Mesylate
Proportional Hazards Models
Anoikis
Drug Screening Assays, Antitumor
Gene Expression
Blotting, Western
Biomarkers, Tumor
Aged
Middle Aged
Enzyme-Linked Immunosorbent Assay
Machine Learning
Breast Neoplasms
Transcriptome
Antineoplastic Agents
Treatment Outcome
Prognosis
Tumor Microenvironment
Real-Time Polymerase Chain Reaction
Gene Expression Profiling
Databases, Genetic
Female
Reverse Transcriptase Polymerase Chain Reaction
Predictive Value of Tests
Reproducibility of Results
Immunotherapy
Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.
Scientific reports
Nov 11, 2024
Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This st...
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