Results 191 to 200 of about 21,280,601 (295)

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Contribution of Structure Learning Algorithms in Social Epidemiology: Application to Real-World Data. [PDF]

Int J Environ Res Public Health
Colineaux H, Lepage B, Chauvin P, Dimeglio C, Delpierre C, Lefèvre T.   +5 more
europepmc   +1 more source

Treatment of missing data in Bayesian network structure learning: an application to linked biomedical and social survey data. [PDF]

BMC Med Res Methodol, 2022
Ke X, Keenan K, Smith VA.
europepmc   +1 more source

Complex Cryptographic and User‐Centric Physically Unclonable Functions Enabled by Strain‐Sensitive Nanocrystals via Selective Ligand Exchange

Advanced Functional Materials, EarlyView.
This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim, Hanseok Seo, Taesung Park, Byung Ku Jung, Junhyeok Park, Young Kyun Choi, Dae Yang Oh, Soong Ju Oh   +7 more
wiley   +1 more source

Assessing Credibility in Bayesian Networks Structure Learning. [PDF]

Entropy (Basel)
Barth V, Serrão F, Maciel C.
europepmc   +1 more source

Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data. [PDF]

Biometrics, 2023
Liu D, Zhao C, He Y, Liu L, Guo Y, Zhang X.   +5 more
europepmc   +1 more source

Peptide Sequencing With Single Acid Resolution Using a Sub‐Nanometer Diameter Pore

Advanced Functional Materials, EarlyView.
To sequence a single molecule of Aβ1−42–sodium dodecyl sulfate (SDS), the aggregate is forced through a sub‐nanopore 0.4 nm in diameter spanning a 4.0 nm thick membrane. The figure is a visual molecular dynamics (VMD) snapshot depicting the translocation of Aβ1−42–SDS through the pore; only the peptide, the SDS, the Na+ (yellow/green) and Cl− (cyan ...
Apurba Paul, Archith Rayabharam, Lisa Almonte, Eveline Rigo, Joshy Joseph, Ashutosh Kumar, Zhuxin Dong, Narayana Aluru, Gregory Timp   +8 more
wiley   +1 more source

Classroom network structure learning engagement and parallel temporal attention LSTM based knowledge tracing. [PDF]

PLoS One
Shou Z, Li Y, Li D, Mo J, Zhang H.
europepmc   +1 more source

Region‐to‐Region Unidirectional Connection In Vitro Brain Model for Studying Directional Propagation of Neuropathologies

Advanced Functional Materials, EarlyView.
A unidirectional cerebral organoid–organoid neural circuit is established using a microfluidic platform, enabling controlled directional propagation of electrical signals, neuroinflammatory cues, and neurodegenerative disease–related proteins between spatially separated organoids.
Kyeong Seob Hwang, Hui‐Wen Liu, Hyogeun Shin, Hyun Wook Kang, Minjin Kang, Jongbaeg Kim, Il‐Joo Cho, Nakwon Choi, Seok Chung, Hong Nam Kim   +9 more
wiley   +1 more source

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis. [PDF]

Appl Intell (Dordr)
Petkov H, MacLellan C, Dong F.
europepmc   +1 more source