AIMC Topic: Neoplasms

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Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review.

Cancer medicine
BACKGROUND: Medical images play an important role in diagnosis and treatment of pediatric solid tumors. The field of radiology, pathology, and other image-based diagnostics are getting increasingly important and advanced. This indicates a need for ad...

Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.

Journal of cellular and molecular medicine
Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational ...

Enhancing Privacy-Preserving Cancer Classification with Convolutional Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Precision medicine significantly enhances patients prognosis, offering personalized treatments. Particularly for metastatic cancer, incorporating primary tumor location into the diagnostic process greatly improves survival rates. However, traditional...

Understanding TCR T cell knockout behavior using interpretable machine learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T cell performance to create more powerful cancer immunotherapies, but understanding the changes to T cell behavior induced by genetic perturbations rema...

Cancer-Associated Lymphoid Aggregates in Histology Images: Manual and Deep Learning-Based Quantification Approaches.

Methods in molecular biology (Clifton, N.J.)
Quantification of lymphoid aggregates including tertiary lymphoid structures (TLS) with germinal centers in histology images of cancer is a promising approach for developing prognostic and predictive tissue biomarkers. In this article, we provide rec...

Transforming Cancer Care: The Impact of AI-driven Strategies.

Current cancer drug targets
AI is a critical component in healthcare, especially in the application of precision medicine where patients' characteristics, including genetic makeup, determine the treatment options that should be implemented. AI sorts big data, predicting people'...

Artificial intelligence-aided data mining of medical records for cancer detection and screening.

The Lancet. Oncology
The application of artificial intelligence methods to electronic patient records paves the way for large-scale analysis of multimodal data. Such population-wide data describing deep phenotypes composed of thousands of features are now being leveraged...

GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.

Journal of cellular and molecular medicine
Multimodal information provides valuable resources for cancer prognosis and survival prediction. However, the computational integration of this heterogeneous data information poses significant challenges due to the complex interactions between molecu...

DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways.

Cancer research communications
DeePathNet integrates cancer-specific biological pathways using transformer-based deep learning for enhanced cancer analysis. It outperforms existing models in predicting drug responses, cancer types, and subtypes. By enabling pathway-level biomarker...

Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 () in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, 's compre...