Results 221 to 230 of about 66,452 (290)

Predictive Modelling of Solvent Effects on Drug Incorporation into Polymeric Nanocarriers: A Machine Learning Approach

Macromolecular Rapid Communications, EarlyView.
When seeking nanoparticles with elevated drug loading content, the experimental setup, including solvent selection, is crucial. Through machine learning, we pinpointed that the drug's solubility in the organic solvent is the key factor for attaining high drug loading content.
Wei Ge, Ramindu De Silva, Yanan Fan, Scott A. Sisson, Martina H. Stenzel   +4 more
wiley   +1 more source

Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods. [PDF]

Neuroinformatics
Kropiunig J, Sørensen Ø.
europepmc   +1 more source

Uncovering Key Characteristics of Antibacterial Peptides through Machine Learning

Macromolecular Rapid Communications, EarlyView.
Machine‐learning (ML) techniques using random forest classification models revealed key characteristics that predict effective antimicrobial peptides (AMPs) targeting Gram‐negative bacteria, Gram‐positive bacteria, and mycobacteria. The ideal cLogP (<$ < $−6) and net‐charge (≤+4) threshold was the same for all three targets with variations in the ...
Jooyoung Roh, Cyrille Boyer, Priyank V. Kumar   +2 more
wiley   +1 more source

Developing and validating a machine learning model for predicting post-thrombolysis seizures in acute ischemic stroke. [PDF]

Medicine (Baltimore)
Jia L, Hu Y, Jin G, Zhou Z.
europepmc   +1 more source

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

Research on a Predictive Model for Microalbuminuria in Type 2 Diabetes Based on Machine Learning and SHAP Analysis. [PDF]

Int J Endocrinol
Liu Z, Zhou Z, Sun Y, Du X, Zhang H, Ji C.   +5 more
europepmc   +1 more source

Active Learning for the Discovery of Antiviral Polymers

Macromolecular Rapid Communications, EarlyView.
Machine learning and active learning are integrated to accelerate the discovery of antiviral polymers. Molecular descriptors derived from polymer composition enable predictive modeling of antiviral activity, while unsupervised clustering explores chemical diversity. The active learning workflow identifies optimal candidates for synthesis, demonstrating
Clodagh M Boland, Nhat Quynh Nguyen, Nathan RB Boase   +2 more
wiley   +1 more source

The Utilization, Application, and Impact of Institutional Special Needs Plans (I-SNPs) in Nursing Facilities: A Rapid Review. [PDF]

Healthcare (Basel)
Mileski M, Shapley R, Beauvais B, Topinka JB, Shanmugam R, Betancourt JA, Brooks M, McClay R.   +7 more
europepmc   +1 more source

Systematic Deciphering of ATBC Nephrotoxicity Mechanisms via Machine Learning and Single‐Cell Analysis

Medicine Bulletin, EarlyView.
This study elucidates the nephrotoxic mechanism of acetyl tributyl citrate (ATBC) by integrating network toxicology, machine learning, and single‑cell multi‑omics analysis to systematically decipher the molecular mechanisms and cell‑type‑specific regulatory networks underlying ATBC‑induced kidney injury. First, a protein‑protein interaction network was
Yimao Wu, Yalun Liang, Jintao Liang, Wenlong Sun, Qian Zhang, Meng‐Yao Li   +5 more
wiley   +1 more source

Decoding heart failure subtypes with neural networks via differential explanation analysis. [PDF]

Brief Bioinform
Ruz Jurado M, Rodriguez Morales D, Genetzakis E, Ardakani FB, Zanders L, Fischer A, Buettner F, Schulz MH, Dimmeler S, John D.   +9 more
europepmc   +1 more source