Bioterrorism

How Artificial Intelligence Unravels the Complex Web of Cancer Drug Response.

The intersection of precision medicine and artificial intelligence (AI) holds profound implications ...

Integration of autoencoder and graph convolutional network for predicting breast cancer drug response.

Breast cancer is the most prevalent type of cancer among women. The effectiveness of anticancer pha...

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.

PURPOSE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer...

Response to "The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a 'scientist'".

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.

STUDY OBJECTIVES: The standard of care for military personnel with insomnia is cognitive behavioral ...

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer trea...

Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.

PURPOSE: Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful bu...

Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hod...

Dental robotics: a groundbreaking technology with disruptive potential - review article.

Dental Robotics represent a groundbreaking technological frontier with the potential to disrupt trad...

Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review.

This narrative review explores the transformative potential of Artificial Intelligence (AI) and adva...

Development of Biomechanical Response Curves for the Calibration of Biofidelic Measuring Devices Used in Robot Collision Testing.

Collaborative robots (cobots) can be employed in close proximity to human workers without safety fen...

Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.

MOTIVATION: Personalized cancer treatments require accurate drug response predictions. Existing deep...

Optimal fusion of genotype and drug embeddings in predicting cancer drug response.

Predicting cancer drug response using both genomics and drug features has shown some success compare...

Improving drug response prediction via integrating gene relationships with deep learning.

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treat...

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal g...

Surgical stress response in robot-assisted versus laparoscopic surgery for colon cancer (SIRIRALS): randomized clinical trial.

BACKGROUND: Evidence for the routine use of robotic technology and its impact on short-term outcomes...

Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment.

BACKGROUND: Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients w...

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.

UNLABELLED: Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunother...

Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients.

BACKGROUND: Breast cancer is a prevalent public health concern affecting numerous women globally and...

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment d...

Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

BACKGROUND: Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with c...