Results 211 to 220 of about 221,929 (334)

Classifier-driven generative adversarial networks for enhanced antimicrobial peptide design. [PDF]

Brief Bioinform
Zervou MA, Doutsi E, Pantazis Y, Tsakalides P.   +3 more
europepmc   +1 more source

Cutting edge strategies for diabetic wound care: Nanotechnology, bioengineering, and beyond

BMEMat, EarlyView.
Graphical abstract illustrates the challenges in diabetic wound healing, covering pathophysiology, formulation hurdles, and emerging therapeutic strategies. It highlights the role of hyperglycemia, formulation complexities, and advanced technologies like bioprinting and AI in improving diabetic wound management. Abstract Diabetic wounds affect millions
Usama Ahmad, Wan Nurhidayah Wan Hanaffi, Anas Islam, Aimen Salman, Mohd Muazzam Khan, Faiyaz Shakeel, Qiang Cai, Xiaojun Cai, Quazi T. H. Shubhra   +8 more
wiley   +1 more source

Dual decoding generative adversarial networks for infrared image enhancement. [PDF]

Sci Rep
Yu Y, Jiang L, Hu Q, Cai Q, Zeng Q, Sun X.   +5 more
europepmc   +1 more source

Time Series Forecasting with Missing Data Using Generative Adversarial Networks and Bayesian Inference [PDF]

Xiaoou Li
openalex   +1 more source

On the Evaluation of Generative Adversarial Networks By Discriminative\n Models [PDF]

, 2020
Amirsina Torfi, Mohammadreza Beyki, Edward A. Fox   +2 more
openalex   +1 more source

Advancing design strategies in smart stimulus‐responsive liposomes for drug release and nanomedicine

BMEMat, EarlyView.
Schematic illustration of stimulus‐responsive liposomes designed for controlled drug release and nanomedicine. The innermost circle represents different liposomal structures, including unilamellar, multilamellar, and multivesicular liposomes. The middle layer illustrates the responsive phospholipid components.
Yuchen Guo, Nan Li, Daiwei Zhang, Jingya Gu, Zhihuan Liao, Zihao Teng, Xiangfu Du, Peter S. Timashev, Shipeng Chen, Shuaidong Huo   +9 more
wiley   +1 more source

Synthetic Orthopantomography Image Generation Using Generative Adversarial Networks for Data Augmentation. [PDF]

Int Dent J
Waqas M, Hasan S, Ghori AF, Alfaraj A, Faheemuddin M, Khurshid Z.   +5 more
europepmc   +1 more source

A Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial Network [PDF]

Abdelfattah Abassi, Brahim Bakkas, Brahim Bakkas, Mostapha El Jai, Mostapha El Jai, Ahmed Arid, Hussain Ben-Azza   +6 more
openalex   +1 more source

Generating Multi-label Discrete Patient Records using Generative Adversarial Networks [PDF]

, 2017
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, Jimeng Sun   +5 more
openalex   +1 more source

Artificial intelligence‐enabled digital biomedical engineering

BMEMat, EarlyView.
This review presents a comprehensive overview, emphasizing the critical role of artificial intelligence (AI) in biomedical engineering. It further explores the implications of AI for future biomedical research and clinical practice, aiming to provide theoretical insights for academic investigation and technological innovation in the field.
Peiran Song, Xuan Tang, Xukun Lv, Rui L. Reis, Xiao Chen, Long Bai, Jiacan Su   +6 more
wiley   +1 more source