Communicated by Ramaswamy H. Sarma.
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http://dx.doi.org/10.1080/07391102.2018.1540361 | DOI Listing |
Brief Bioinform
November 2024
Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFJMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Introduction: Active learning can significantly decrease the labeling cost of deep learning workflows by prioritizing the limited labeling budget to high-impact data points that have the highest positive impact on model accuracy. Active learning is especially useful for semantic segmentation tasks where we can selectively label only a few high-impact regions within these high-impact images. Most established regional active learning algorithms deploy a static-budget querying strategy where a fixed percentage of regions are queried in each image.
View Article and Find Full Text PDFFood Sci Nutr
January 2025
Nutrition and Bromatology Group, Department of Analytical Chemistry and Food Science, Instituto de Agroecoloxía e Alimentación (IAA) - CITEXVI Universidade de Vigo Vigo Spain.
The Asteraceae family has been of significant concern for ethnobotanical studies, thanks to its health-promoting properties linked to a plethora of bioactive compounds, among which phenolic compounds play a critical role. In this work, a workflow based on computational chemometrics was employed to assess the authenticity and biomarker search of five key Asteraceae species commonly employed in traditional medicine. The UHPLC-DAD-ESI/MS-MS phenolic profile of Asteraceae extracts was combined with the evaluation of several in vitro biological properties.
View Article and Find Full Text PDFFront Cell Dev Biol
January 2025
Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
Introduction: Diabetic retinopathy (DR) has long been recognized as a common complication of diabetes, making accurate automated grading of its severity essential. Color fundus photographs play a crucial role in the grading of DR. With the advancement of artificial intelligence technologies, numerous researchers have conducted studies on DR grading based on deep features and radiomic features extracted from color fundus photographs.
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