Apoptosis

Comprehensive multi-omics analysis identifies chromatin regulator-related signatures and TFF1 as a therapeutic target in lung adenocarcinoma through a 429-combination machine learning approach.

Frontiers in immunology 39539551

INTRODUCTION: Lung cancer is a leading cause of cancer-related deaths, with its incidence continuing to rise. Chromatin remodeling, a crucial process in gene expression regulation, plays a significant role in the development and progression of malign...

Adenocarcinoma of Lung Animals Apoptosis Biomarkers, Tumor Cell Line, Tumor Cell Proliferation Chromatin Female Gene Expression Regulation, Neoplastic Humans Lung Neoplasms Machine Learning Mice Multiomics Prognosis Trefoil Factor-1 Tumor Microenvironment Xenograft Model Antitumor Assays

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Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.

Frontiers in immunology 39346927

INTRODUCTION: The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies...

Adenocarcinoma of Lung Apoptosis Biomarkers, Tumor Computational Biology Female Gene Expression Regulation, Neoplastic Humans Lung Neoplasms Machine Learning Male Multiomics Prognosis

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Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach.

Frontiers in endocrinology 39149122

BACKGROUND: Endometriosis (EM) is a prevalent gynecological disorder frequently associated with irregular menstruation and infertility. Programmed cell death (PCD) is pivotal in the pathophysiological mechanisms underlying EM. Despite this, the preci...

Apoptosis Biomarkers Endometriosis Female Gene Expression Profiling Humans Machine Learning Mendelian Randomization Analysis Molecular Docking Simulation Pyruvate Dehydrogenase Acetyl-Transferring Kinase

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Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Journal of cellular and molecular medicine 39011666

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating ...

Apoptosis Biomarkers, Tumor Carcinoma, Renal Cell Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Kidney Neoplasms Machine Learning Prognosis Single-Cell Analysis Tumor Microenvironment

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A prognostic biomarker of disulfidptosis constructed by machine learning framework model as potential reporters of pancreatic adenocarcinoma.

Cellular signalling 39209222

BACKGROUND: Pancreatic adenocarcinoma (PAAD), known for its high lethality, has not been thoroughly explored in terms of its mechanisms and patterns of immune infiltration. Disulfidptosis, a newly identified mode of cell death, is likely associated w...

Adenocarcinoma Apoptosis Biomarkers, Tumor Cell Line, Tumor Gene Expression Regulation, Neoplastic Humans Machine Learning Pancreatic Neoplasms Prognosis Proto-Oncogene Proteins c-met

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Machine learning-based integration develops a multiple programmed cell death signature for predicting the clinical outcome and drug sensitivity in colorectal cancer.

Anti-cancer drugs 39132895

Tumorigenesis and treatment are closely associated with various programmed cell death (PCD) patterns. However, the coregulatory role of multiple PCD patterns in colorectal cancer (CRC) remains unknown. In this study, we developed a multiple PCD index...

Antineoplastic Combined Chemotherapy Protocols Apoptosis Colorectal Neoplasms Drug Resistance, Neoplasm Female Humans Immune Checkpoint Inhibitors Machine Learning Male Nomograms Prognosis

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Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns.

Scientific reports 39090256

Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and...

Apoptosis Biomarkers, Tumor Cell Death Female Humans Leukemia, Myeloid, Acute Machine Learning Male Middle Aged Prognosis Transcriptome

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Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods.

Computational biology and chemistry 39265463

Apoptotic proteins play a crucial role in the apoptosis process, ensuring a balance between cell proliferation and death. Thus, further elucidating the regulatory mechanisms of apoptosis will enhance our understanding of their functions. However, the...

Apoptosis Computational Biology Humans Machine Learning Support Vector Machine

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The diagnostic value investigation of programmed cell death genes in heart failure.

BMC cardiovascular disorders 39574022

BACKGROUND: We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF).

Apoptosis Apoptosis Regulatory Proteins Case-Control Studies Databases, Genetic Gene Expression Profiling Gene Expression Regulation Gene Regulatory Networks Genetic Markers Heart Failure Humans Machine Learning Nomograms Plasminogen Activator Inhibitor 1 Predictive Value of Tests Protein Interaction Maps Reproducibility of Results Transcriptome

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Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.

Urolithiasis 39549053

Calcium oxalate (CaOx) nephrolithiasis constitutes approximately 75% of nephrolithiasis cases, resulting from the supersaturation and deposition of CaOx crystals in renal tissues. Despite their prevalence, precise biomarkers for CaOx nephrolithiasis ...

Animals Apoptosis Biomarkers Calcium Oxalate Disease Models, Animal Humans Machine Learning Male Nephrolithiasis Rats Rats, Sprague-Dawley Tumor Protein p73

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Explore the latest research on artificial intelligence and machine learning in medicine.

Comprehensive multi-omics analysis identifies chromatin regulator-related signatures and TFF1 as a therapeutic target in lung adenocarcinoma through a 429-combination machine learning approach.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.

Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

A prognostic biomarker of disulfidptosis constructed by machine learning framework model as potential reporters of pancreatic adenocarcinoma.

Machine learning-based integration develops a multiple programmed cell death signature for predicting the clinical outcome and drug sensitivity in colorectal cancer.

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns.

Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods.

The diagnostic value investigation of programmed cell death genes in heart failure.

Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.

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