AIMC Topic: Biomarkers, Tumor

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Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis.

BMC cancer
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...

Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence.

Scientific reports
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...

Machine learning developed regulatory T cells-related signature for prognosis and immunotherapy benefit in oral squamous cell carcinoma.

American journal of otolaryngology
BACKGROUND: Oral squamous cell carcinoma (OSCC) is one of the most common malignancies with poor clinical outcome. Regulatory T cells (Tregs) have a dual role in maintaining immune homeostasis and suppressing anti-tumor immunity. The role of Tregs re...

Electrochemical microfluidic biosensors for the detection of cancer biomarker miRNAs.

Talanta
Cancer is a formidable adversary in contemporary healthcare. Routine screening and early diagnosis are crucial for favourable therapeutic outcomes. Publications, clinical trials, and patent landscape analysis suggest miRNA as promising biomarkers for...

GLIO-Select: Machine Learning-Based Feature Selection and Weighting of Tissue and Serum Proteomic and Metabolomic Data Uncovers Sex Differences in Glioblastoma.

International journal of molecular sciences
Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better survival outcomes compared to males. While sex differences in GBM have been observed, the underlying biolo...

Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

Journal of cancer research and therapeutics
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).

Development and Validation of a Cell-Free DNA Fragmentomics-Based Model for Early Detection of Pancreatic Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Pancreatic ductal adenocarcinoma (PDAC), known for its high fatality rate, is often diagnosed in its advanced stages where surgical options are not viable. This highlights the critical need for innovative and effective early detection techni...

Development and validation of a machine learning-based risk model for metastatic disease in nmCRPC patients: a tumor marker prognostic study.

International journal of surgery (London, England)
BACKGROUND: Nonmetastatic castration-resistant prostate cancer (nmCRPC) is a clinical challenge due to the high progression rate to metastasis and mortality. To date, no prognostic model has been developed to predict the metastatic probability for nm...