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Artificial intelligence in clinical genetics.
Eur J Hum Genet
January 2025
Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Artificial intelligence (AI) has been growing more powerful and accessible, and will increasingly impact many areas, including virtually all aspects of medicine and biomedical research. This review focuses on previous, current, and especially emerging applications of AI in clinical genetics. Topics covered include a brief explanation of different general categories of AI, including machine learning, deep learning, and generative AI.
View Article and Find Full Text PDFThe third-wave society!
J Indian Soc Pedod Prev Dent
October 2024
Editor-in-Chief, Journal of Indian Society of Pedodontics and Preventive Dentistry. E-mail:
Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation.
Med Image Anal
December 2024
Department of Electrical Engineering, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA. Electronic address:
Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift problem for cross-modality medical segmentation. However, most of the existing UDA approaches depend on high-quality image translation with diversity constraints to explicitly augment the potential data diversity, which is hard to ensure semantic consistency and capture domain-invariant representation. In this paper, free of image translation and diversity constraints, we propose a novel Style Mixup Enhanced Disentanglement Learning (SMEDL) for UDA medical image segmentation to further improve domain generalization and enhance domain-invariant learning ability.
View Article and Find Full Text PDFOccasioned by the publication of , the text represents exchanges held in the summer of 2024 at the invitation of . It addresses the genesis and the history of the project, and its impact on psychoanalytic rethinking, formation, and practice. While exploring the potential and the limits of revisioning, it also raises questions about the nature of transmission of psychoanalytic knowledge and about the field's relation to the state of its own standards in the age of Anthropocene.
View Article and Find Full Text PDFmiRNA-target complementarity in cnidarians resembles its counterpart in plants.
EMBO Rep
January 2025
Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, Faculty of Science, Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.
microRNAs (miRNAs) are important post-transcriptional regulators that activate silencing mechanisms by annealing to mRNA transcripts. While plant miRNAs match their targets with nearly-full complementarity leading to mRNA cleavage, miRNAs in most animals require only a short sequence called 'seed' to inhibit target translation. Recent findings showed that miRNAs in cnidarians, early-branching metazoans, act similarly to plant miRNAs, by exhibiting full complementarity and target cleavage; however, it remained unknown if seed-based regulation was possible in cnidarians.
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