Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.forsciint.2019.02.045 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans.
Curr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
Evaluating the Immunogenicity Risk of Protein Therapeutics by Augmenting T Cell Epitope Prediction with Clinical Factors.
AAPS J
January 2025
Department of BioAnalytical Sciences, Genentech Inc, South San Francisco, California, USA.
Protein-based therapeutics may elicit undesired immune responses in a subset of patients, leading to the production of anti-drug antibodies (ADA). In some cases, ADAs have been reported to affect the pharmacokinetics, efficacy and/or safety of the drug. Accurate prediction of the ADA response can help drug developers identify the immunogenicity risk of the drug candidates, thereby allowing them to make the necessary modifications to mitigate the immunogenicity.
View Article and Find Full Text PDFPerformance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.
Neurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
View Article and Find Full Text PDFML-based tooth shade assessment to prevent metamerism in different clinic lights.
Lasers Med Sci
January 2025
Erzincan University, 24002, Erzincan, Turkey.
The aesthetic understanding has found its place in dental clinics and prosthetic dental treatment. Determining the appropriate prosthetic tooth color between the clinician, patient and technician is a difficult process due to metamerism. Metamerism, known as the different perception of the color of an object under different light sources, is caused by the lighting differences between the laboratory and the dental clinic.
View Article and Find Full Text PDFIntegrative bioinformatics approaches reveal key hub genes in cyanobacteria: insights from Synechocystis sp. PCC 6803 and Geminocystis sp. NIES-3708 under abiotic stress conditions.
Genes Genomics
January 2025
Department of Molecular Biosciences, Wenner-Gren Institute, Stockholm University, 106 91, Stockholm, Sweden.
Background: Cyanobacteria, particularly Synechocystis sp. PCC 6803, serve as model organisms for studying acclimation strategies that enable adaptation to various environmental stresses. Understanding the molecular mechanisms underlying these adaptations provides insight into how cells adjust gene expression in response to challenging conditions.
View Article and Find Full Text PDFWant 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!