Results 281 to 290 of about 3,002,307 (310)

Machine‐Learning‐Aided Advanced Electrochemical Biosensors

Advanced Materials, EarlyView.
Electrochemical biosensors are highly sensitive, portable, and versatile. Advanced nanomaterials enhance their performance, while machine learning (ML) improves data analysis, minimizes interference, and optimizes sensor design. Despite progress in both fields, their combined potential in diagnostics remains underexplored.
Andrei Bocan, Roozbeh Siavash Moakhar, Carolina del Real Mata, Max Petkun, Tristan De Iure‐Grimmel, Sripadh Guptha Yedire, Hamed Shieh, Arash Khorrami Jahromi, Sahar Sadat Mahshid, Sara Mahshid   +9 more
wiley   +1 more source

Classification of Apricot Varieties by Infrared Spectroscopy and Machine Learning. [PDF]

ACS Agric Sci Technol
Béjar-Grimalt J, Pérez-Guaita D, Sánchez-Illana Á, García-Contreras R, Kataria R, Bureau S, de la Guardia M, Cadet F.   +7 more
europepmc   +1 more source

Untangling profiles of postthrombotic syndrome using unsupervised machine learning. [PDF]

Blood Adv
Iding AFJ, Ten Cate V, Ten Cate H, Wild PS, Ten Cate-Hoek AJ.   +4 more
europepmc   +1 more source

Editorial: Data science and machine learning for psychological research. [PDF]

Front Psychol
Yu CH.
europepmc   +1 more source

Correction to "Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations". [PDF]

CPT Pharmacometrics Syst Pharmacol
europepmc   +1 more source

“Double Machine Learning for Causal Inference in High-Dimensional Electronic Health Records”

Du M, Guo Y, Li X, Catala M, Pareto-Alhambra D.   +4 more
europepmc   +1 more source