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Biomarkers, Tumor

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Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabiliti...

CCPred: Global and population-specific colorectal cancer prediction and metagenomic biomarker identification at different molecular levels using machine learning techniques.

Computers in biology and medicine
Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the comp...

From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.

Nature protocols
Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. Ho...

Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer.

Scientific reports
Cancer stem cells (CSCs) have the potential to self-renew and induce cancer, which may contribute to a poor prognosis by enabling metastasis, recurrence, and therapy resistance. Hence, this study was performed to identify the association between CSC-...

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
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...

A multi-task deep learning model based on comprehensive feature integration and self-attention mechanism for predicting response to anti-PD1/PD-L1.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitor (ICI) has been widely used in the treatment of advanced cancers, but predicting their efficacy remains challenging. Traditional biomarkers are numerous but exhibit heterogeneity within populations. For comprehe...

Scaling data toward pan-cancer foundation models.

Trends in cancer
Recent advances in artificial intelligence (AI) have revolutionized computational pathology (CPath), particularly through deep learning (DL) and neural networks (NNs). In a recent study, Vorontsov et al. introduced Virchow, a new foundation model (FM...

piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer.

Molecules (Basel, Switzerland)
Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs-piwi-RNAs (piRNAs)-and their transcripts, we sought to identify if these piRNAs could be used as biomar...