Overview of deep learning-based cell image analysis. A typical analysis pipeline consists of a retraining module and an inference module: the inference module directly produces estimated metrics.

Recent advancements in deep learning have transformed the analysis of blood cell images and the classification of leukemia. By employing complex neural network architectures, such as convolutional ...

A computational method called scSurv, developed by researchers at Institute of Science Tokyo, links individual cells to patient outcomes using widely available bulk RNA sequencing data. The approach ...

Using Early Biomarker Change and Treatment Adherence to Predict Risk of Relapse Among Patients With Chronic Myeloid Leukemia Who Are in Remission The imaging cohort consisted of positron emission ...

Completed phase 1a dose escalation study of the first oral ENPP1 inhibitor RBS2418 immunotherapy in subjects with metastatic solid tumors. SECN-15: A novel treatment option for patients with ...

The research team led by Senior Researchers Yoonhee Lee from the Division of Biomedical Technology and Gyogwon Koo from the Division of Intelligent Robot at DGIST (under President Kunwoo Lee) has ...

A biology-guided artificial intelligence model applied to routine pathology slides accurately predicted outcomes and response ...

The rapid advancement of spatial and single-cell omics technologies has revolutionized molecular biosciences by enabling high-resolution profiling of gene ...

During early development, tissues and organs begin to form through the shifting, splitting, and growing of many thousands of cells. A team of researchers headed by MIT engineers has now developed a ...

The scSurv is a method that deconvolutes bulk RNA-seq data into individual single cells using scRNA-seq data and performs survival analysis with an extended Cox proportional hazards model. This ...