AIMC Topic: Biomarkers, Tumor

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The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.

Frontiers in immunology
Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM...

Analysis of Tumor Microenvironment Characteristics in Bladder Cancer: Implications for Immune Checkpoint Inhibitor Therapy.

Frontiers in immunology
The tumor microenvironment (TME) plays a crucial role in cancer progression and recent evidence has clarified its clinical significance in predicting outcomes and efficacy. However, there are no studies on the systematic analysis of TME characteristi...

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.

Biomolecules
Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient's quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount o...

Machine learning-based investigation of the cancer protein secretory pathway.

PLoS computational biology
Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is ofte...

IL-8, MSPa, MIF, FGF-9, ANG-2 and AgRP collection were identified for the diagnosis of colorectal cancer based on the support vector machine model.

Cell cycle (Georgetown, Tex.)
Colorectal cancer (CRC) is one of the most common cancer, and the early detection of CRC is essential to improve the survival rate of patients. To identify diagnostic markers for colorectal cancer (CRC) by screening differentially expressed proteins ...

Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data.

European journal of cancer (Oxford, England : 1990)
PURPOSE: Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts.

Predicting treatment response from longitudinal images using multi-task deep learning.

Nature communications
Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imaging response metrics do not reliably predict the underlying biological response. Here, we present a multi-task deep learning approach that allows simul...

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer.

Scientific reports
This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy...

Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts.

Scientific reports
Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's...

Novel gene signatures for stage classification of the squamous cell carcinoma of the lung.

Scientific reports
The squamous cell carcinoma of the lung (SCLC) is one of the most common types of lung cancer. As GLOBOCAN reported in 2018, lung cancer was the first cause of death and new cases by cancer worldwide. Typically, diagnosis is made in the later stages ...