Artificial intelligence is now designing custom proteins in seconds—a process that once took years—paving the way for cures to diseases like cancer and antibiotic-resistant infections. Australian ...

Dr. Ho Sang Jung and his research team from the Advanced Bio and Healthcare Materials Research Division at the Korea Institute of Materials Science(KIMS) have developed an optical biosensor capable of ...

Dr. Ho Sang Jung and his research team from the Advanced Bio and Healthcare Materials Research Division at the Korea ...

This study's objective is to evaluate the degree of accuracy of several machine learning algorithms for detecting early-stage lung cancer. A thorough investigation showed that certain classifiers ...

Early detection is pivotal, as survival rates exceed 90% when the disease is identified in its early stages. In response to this critical need, we introduce WFC2DS (Web Framework for Cervical Cancer ...

To our knowledge, no large-scale, multicenter study has comprehensively analyzed the various dimension predictors of adherence to electronic patient-reported outcomes (ePRO)-guided nutritional ...

DNA/MXene biocomposite gas sensing biosensor array machine learning noninvasive cancer recognition Read this article To access this article, please review the available access options below.

In an interview with Applied Clinical Trials, Brian Ongioni, chief product officer at uMotif, explained how the company integrates patient and site feedback early in the development process to improve ...

A new cancer drug candidate has demonstrated the ability to block tumor growth without triggering a common and debilitating side effect.

Objectives To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT. Methods A systematic review of CE-marked, AI-based software for ...

By integrating nanotechnology with machine learning, we have developed a promising approach for the early detection of pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer (OV).

A complete pipeline for network intrusion detection comparing label encoding and one‑hot encoding, with SMOTE resampling, feature selection, and ensemble modeling using scikit‑learn and XGBoost, also ...