AIMC Topic: Neoplasms

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General Applicability of Existing College of American Pathologists Accreditation Requirements to Clinical Implementation of Machine Learning-Based Methods in Molecular Oncology Testing.

Archives of pathology & laboratory medicine
CONTEXT.—: The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based model...

Mapping intellectual structure and research hotspots of cancer studies in primary health care: A machine-learning-based analysis.

Medicine
In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. This study, as one of the most detailed investigations into cancer research in primary care, comprehen...

Federated transfer learning with differential privacy for multi-omics survival analysis.

Briefings in bioinformatics
Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of features is significantly larger than the sample size, making the integration of multi-omics data for survival analysis of a specific cancer particularly ...

Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.

Briefings in bioinformatics
Synthetic lethality (SL) is a promising gene interaction for cancer therapy. Recent SL prediction methods integrate knowledge graphs (KGs) into graph neural networks (GNNs) and employ attention mechanisms to extract local subgraphs as explanations fo...

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.

Briefings in bioinformatics
Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as und...

PCLSurv: a prototypical contrastive learning-based multi-omics data integration model for cancer survival prediction.

Briefings in bioinformatics
Accurate cancer survival prediction remains a critical challenge in clinical oncology, largely due to the complex and multi-omics nature of cancer data. Existing methods often struggle to capture the comprehensive range of informative features requir...

Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data.

Briefings in bioinformatics
Accurate cancer prognosis is essential for personalized clinical management, guiding treatment strategies and predicting patient survival. Conventional methods, which depend on the subjective evaluation of histopathological features, exhibit signific...

Scaling up drug combination surface prediction.

Briefings in bioinformatics
Drug combinations are required to treat advanced cancers and other complex diseases. Compared with monotherapy, combination treatments can enhance efficacy and reduce toxicity by lowering the doses of single drugs-and there especially synergistic com...

Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.

Briefings in bioinformatics
High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primarily focus on feature extraction or employ simplistic fully connected layers within prognostic modules,...

AI meets informed consent: a new era for clinical trial communication.

JNCI cancer spectrum
Clinical trials are fundamental to evidence-based medicine, providing patients with access to novel therapeutics and advancing scientific knowledge. However, patient comprehension of trial information remains a critical challenge, as registries like ...