AIMC Topic: Neoplasms

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Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images.

IEEE journal of biomedical and health informatics
In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel...

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

IEEE journal of biomedical and health informatics
Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination predictio...

Understanding the role of the gut microbiome in solid tumor responses to immune checkpoint inhibitors for personalized therapeutic strategies: a review.

Frontiers in immunology
Immunotherapy, especially immune checkpoint inhibitor (ICI) therapy, has yielded remarkable outcomes for some patients with solid cancers, but others do not respond to these treatments. Recent research has identified the gut microbiota as a key modul...

Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models.

Scientific reports
This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorica...

The global trends and distribution in tumor-infiltrating lymphocytes over the past 49 years: bibliometric and visualized analysis.

Frontiers in immunology
BACKGROUND: The body of research on tumor-infiltrating lymphocytes (TILs) is expanding rapidly; yet, a comprehensive analysis of related publications has been notably absent.

Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology.

Medical image analysis
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide...

Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations.

Nature medicine
Randomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk r...

Artificial Intelligence and Cancer Health Equity: Bridging the Divide or Widening the Gap.

Current oncology reports
PURPOSE OF REVIEW: This review aims to evaluate the impact of artificial intelligence (AI) on cancer health equity, specifically investigating whether AI is addressing or widening disparities in cancer outcomes.

Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Cancer cell
Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suita...

pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.

Scientific reports
Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high cost...