Scientists’ Side Hustle? Using AI and Quantum Computing to Generate New Peptides

Researchers cobbled together funding and time to show how quantum computing could aid in the development of drugs to help ...
News-Medical.Net

Machine learning redefines precision cancer therapy using smart radiopharmaceuticals

Designed Radiopharmaceuticals: How Machine Learning Is Redefining Precision Cancer Therapy" reports on the integration of deep learning and generative AI in radiopharmaceutical medicine, its impact on ...
News-Medical.Net

Machine learning framework enables low-cost water toxin detection

Portable screen-printed carbon electrode (SPCE) biosensors offer a rapid and low-cost way to detect microcystin-lysine-arginine (MC-LR), an extremely potent toxin produced by cyanobacteria during ...
eLife

Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions

Celldetective is an open-source software integrating segmentation, tracking, and event detection to perform high-throughput end-to-end study of dynamic cell interactions, without requiring coding ...
GitHub

BreastDCEDL - 2,072 Breast Cancer Patients

The MinCrop version provides three methodicaly selected DCE-MRI time points (pre-contrast, early post-contrast, late post-contrast) cropped to 256×256 pixels around the main tumor. This version has ...
ascopubs.org

Deep-Learning Model for Real-Time Prediction of Recurrence in Early-Stage Non–Small Cell Lung Cancer: A Multimodal Approach (RADAR CARE Study)

Physiologically Based Pharmacokinetic Model to Assess the Drug-Drug-Gene Interaction Potential of Belzutifan in Combination With Cyclin-Dependent Kinase 4/6 Inhibitors A total of 14,177 patients were ...
GitHub

In silico labeling: Predicting fluorescent labels in unlabeled images

This is the code for In silico labeling: Predicting fluorescent labels in unlabeled images. It is the result of a collaboration between Google Accelerated Science and two external labs: the Lee Rubin ...
C&EN

MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells

Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States ...
IEEE

Harnessing Deep Learning for Precise Diagnosis of 6 types of Cancers

Abstract: The use of deep learning in cancer detection has the potential to lead to more precise and timely diagnosis. In order to identify cancer, this study presents a deep learning-based picture ...
Frontiers

A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches

Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, ...