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Biomarkers, Tumor

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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data.

Cancer control : journal of the Moffitt Cancer Center 40334702
IntroductionEarly diagnosis of colorectal cancer (CRC) poses a significant clinical challenge. This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data.MethodsThis retrospective, single-center stu...
Aged Biomarkers, Tumor Carcinoembryonic Antigen Case-Control Studies Colorectal Neoplasms Early Detection of Cancer Female Humans Machine Learning Male MicroRNAs Middle Aged Occult Blood Retrospective Studies
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Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery.

Frontiers in immunology 40330458
OBJECTIVE: This study aimed to identify and analyze immunogenic cell death (ICD)-related multi-omics features in bladder cancer (BLCA) using single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. By integrating these datasets, we sought to con...
Biomarkers, Tumor Computational Biology Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Machine Learning Male Multiomics Precision Medicine Prognosis Single-Cell Analysis Tumor Microenvironment Urinary Bladder Neoplasms
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The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.

Frontiers in immunology 40313958
OBJECTIVE: Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel insights finto its molecular mechanisms. This study aims to investigate the relationship between gene e...
Adenocarcinoma of Lung Autophagy Biomarkers, Tumor Computational Biology Databases, Genetic Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Humans Lung Neoplasms Machine Learning Mitochondria Mitophagy
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Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

International journal of molecular sciences 40332516
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...
Adolescent Adult Alanine Transaminase Alkaline Phosphatase alpha-Fetoproteins Biomarkers, Tumor Carcinoma, Hepatocellular Child Child, Preschool Early Detection of Cancer Female Follow-Up Studies Humans Infant Infant, Newborn Liver Neoplasms Longitudinal Studies Machine Learning Male Middle Aged Retrospective Studies Tyrosinemias Young Adult
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Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.

International journal of molecular sciences 40332226
Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biom...
Biomarkers, Tumor Breast Neoplasms Cell Death Female Gene Expression Regulation, Neoplastic Humans Machine Learning Middle Aged Neoadjuvant Therapy Prognosis Sequence Analysis, RNA Single-Cell Analysis
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Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis.

BMC cancer 40329245
Non-coding RNAs (ncRNAs) play a crucial role in breast cancer progression, necessitating advanced computational approaches for precise disease classification. This study introduces a Deep Reinforcement Learning (DRL)-based framework for predicting nc...
Biomarkers, Tumor Breast Neoplasms Computational Biology Deep Learning Female Humans Metaplasia Prognosis RNA, Long Noncoding RNA, Untranslated Support Vector Machine
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Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.

BMC cancer 40329193
BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machin...
Adult Aged Biomarkers, Tumor Female Humans Lymph Nodes Lymphatic Metastasis Machine Learning Male Middle Aged Neoadjuvant Therapy Prognosis Radiomics Retrospective Studies ROC Curve Stomach Neoplasms Tomography, X-Ray Computed
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Integrative bioinformatics and machine learning identify key crosstalk genes and immune interactions in head and neck cancer and Hodgkin lymphoma.

Scientific reports 40328901
Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with complex molecular underpinnings. Hodgkin lymphoma (HL), another distinct cancer type, shares several biological characteristics with HNSCC, particularly regarding im...
Biomarkers, Tumor Computational Biology Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Head and Neck Neoplasms Hodgkin Disease Humans Machine Learning Prognosis Protein Interaction Maps Squamous Cell Carcinoma of Head and Neck Tumor Microenvironment
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Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence.

Scientific reports 40328883
Acute myeloid leukemia (AML) is a severe hematological malignancy characterized by high recurrence rates, especially in pediatric patients, highlighting the need for reliable prognostic markers. This study proposes methylation signatures associated w...
Adolescent Algorithms Biomarkers, Tumor Child Child, Preschool DNA Methylation Female Humans Infant Leukemia, Myeloid, Acute Machine Learning Male Neoplasm Recurrence, Local Prognosis
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Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

Journal of cancer research and therapeutics 40317140
BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) oncogene status and predict targeted therapy response in colorectal cancer liver metastases (CRLM).
Adult Aged Biomarkers, Tumor Colorectal Neoplasms Deep Learning Female Genes, ras Humans Liver Neoplasms Male Middle Aged Mutation Prognosis Retrospective Studies ROC Curve Tomography, X-Ray Computed
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