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Gene Expression Regulation, Neoplastic

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Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.

International journal of molecular sciences
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...

Ferroptosis-related biomarkers for adamantinomatous craniopharyngioma treatment: conclusions from machine learning techniques.

Frontiers in endocrinology
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...

Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.

Scientific reports
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...

Expression of Salivary miRNAs, Clinical, and Demographic Features in the Early Detection of Gastric Cancer: A Statistical and Machine Learning Analysis.

Journal of gastrointestinal cancer
OBJECTIVE: Gastric cancer ranks as one of the top five deadliest cancers worldwide and is often diagnosed at late stages. Analysis of saliva may provide a non-invasive approach for detection of malignancies in organs associated with the oral cavity. ...

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
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...

Identification of novel markers for neuroblastoma immunoclustering using machine learning.

Frontiers in immunology
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
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),...

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

BioFactors (Oxford, England)
Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph n...

Identification of ferroptosis-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

International journal of biological macromolecules
Ferroptosis has emerged as a critical mechanism in the development and progression of various tumors, particularly diffuse large B-cell lymphoma (DLBCL). However, the thorough characterization of ferroptosis-related genes in DLBCL remains inadequatel...