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Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

Nature communications
Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell c...

Cross-modal alignment and contrastive learning for enhanced cancer survival prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Integrating multimodal data, such as pathology images and genomics, is crucial for understanding cancer heterogeneity, personalized treatment complexity, and enhancing survival prediction. However, most current prognostic me...

Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data.

F1000Research
BACKGROUND: Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large ...

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.

IEEE transactions on neural networks and learning systems
Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to identify disease-associated genes to estimate cancer mortality risk remains challenging...

Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis.

IEEE transactions on medical imaging
Recently, we have witnessed impressive achievements in cancer survival analysis by integrating multimodal data, e.g., pathology images and genomic profiles. However, the heterogeneity and high dimensionality of these modalities pose significant chall...

Generative AI chatbots for reliable cancer information: Evaluating web-search, multilingual, and reference capabilities of emerging large language models.

European journal of cancer (Oxford, England : 1990)
Recent advancements in large language models (LLMs) enable real-time web search, improved referencing, and multilingual support, yet ensuring they provide safe health information remains crucial. This perspective evaluates seven publicly accessible L...

NLP for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review.

Journal of pain and symptom management
This review examines the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. It addresses gaps in existing literature by providing a broader perspective than previo...

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.

Nature cancer
Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-...

ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.

PLoS computational biology
Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to...

A novel approach in cancer diagnosis: integrating holography microscopic medical imaging and deep learning techniques-challenges and future trends.

Biomedical physics & engineering express
Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ult...