Nanobodies, derived from camelids and sharks, offer compact, single-variable heavy-chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, and computational methodologies. Integrating experimental and computational approaches enhances understanding of nanobody structure and function. Future trends involve leveraging next-generation sequencing, machine learning, and artificial intelligence for efficient candidate selection and predictive modeling. The convergence of traditional and computational methods promises revolutionary advancements in precision biomedical applications such as targeted drug delivery and diagnostics. Embracing these technologies accelerates nanobody development, driving transformative breakthroughs in biomedicine and paving the way for precision medicine and biomedical innovation.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/bit.28816 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches.
Biodegradation
December 2024
Department of Civil engineering, Islamic Azad university, Mashhad Branch, Iran.
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).
View Article and Find Full Text PDFMachine Learning Boosted Entropy-Engineered Synthesis of CuCo Nanometric Solid Solution Alloys for Near-100% Nitrate-to-Ammonia Selectivity.
ACS Appl Mater Interfaces
December 2024
Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, 214122 Jiangsu, China.
Nanometric solid solution alloys are utilized in a broad range of fields, including catalysis, energy storage, medical application, and sensor technology. Unfortunately, the synthesis of these alloys becomes increasingly challenging as the disparity between the metal elements grows, due to differences in atomic sizes, melting points, and chemical affinities. This study utilized a data-driven approach incorporating sample balancing enhancement techniques and multilayer perceptron (MLP) algorithms to improve the model's ability to handle imbalanced data, significantly boosting the efficiency of experimental parameter optimization.
View Article and Find Full Text PDFA novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.
J Am Med Inform Assoc
December 2024
AI for Health Institute, Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
View Article and Find Full Text PDFDe-biasing the bias: methods for improving disparity assessments with noisy group measurements.
Biometrics
October 2024
RAND Corporation, Pittsburgh, PA 15213, United States.
Health care decisions are increasingly informed by clinical decision support algorithms, but these algorithms may perpetuate or increase racial and ethnic disparities in access to and quality of health care. Further complicating the problem, clinical data often have missing or poor quality racial and ethnic information, which can lead to misleading assessments of algorithmic bias. We present novel statistical methods that allow for the use of probabilities of racial/ethnic group membership in assessments of algorithm performance and quantify the statistical bias that results from error in these imputed group probabilities.
View Article and Find Full Text PDF25th National and 11th International Annual Congress on Research and Technology of Iranian Medical Sciences Students, Urmia, Iran, 5-7 September, 2024.
Iran Biomed J
December 2024
Student Research Committee , Department of Nursing, Khalkhal University of Medical Sciences, Khalkhal, Iran.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!