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Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: We developed a predictive model to assess the risk of major bleeding (MB) within 6 months of primary venous thromboembolism (VTE) in cancer patients receiving anticoagulant treatment. We also sought to describe the prevalence and incidence o...

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...

Bioinformatics challenges for profiling the microbiome in cancer: pitfalls and opportunities.

Trends in microbiology
Increasing evidence suggests that the human microbiome plays an important role in cancer risk and treatment. Untargeted 'omics' techniques have accelerated research into microbiome-cancer interactions, supporting the discovery of novel associations a...

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...

Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs.

PloS one
Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from researchers and practitioners. While structured data have played a vital role thus far, unstructured d...

The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy.

Computers in biology and medicine
This narrative review examines the promising potential of integrating artificial intelligence (AI) with CRISPR-Cas9 genome editing to advance CAR T-cell therapy. AI algorithms offer unparalleled precision in identifying genetic targets, essential for...

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Journal of medicinal chemistry
Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessen...

Etiological stratification and prognostic assessment of haemophagocytic lymphohistiocytosis by machine learning on onco-mNGS data and clinical data.

Frontiers in immunology
INTRODUCTION: Hemophagocytic lymphohistiocytosis (HLH) is a rare, complicated and life threatening hyperinflammatory syndrome that maybe triggered by various infectious agents, malignancies and rheumatologic disorders. Early diagnosis and identificat...

In silico assessments of the small molecular boron agents to pave the way for artificial intelligence-based boron neutron capture therapy.

European journal of medicinal chemistry
Boron neutron capture therapy (BNCT) is a highly targeted, selective and effective technique to cure various types of cancers, with less harm to the healthy cells. In principle, BNCT treatment needs to distribute the boron (B) atoms inside the tumor ...

MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate prognosis prediction for cancer patients plays a significant role in the formulation of treatment strategies, considerably impacting personalized medicine. Recent advancements in this field indicate that integrating...