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Deep learning for genomics [PDF]
Nature Genetics, 2018 Application of deep learning to genomic datasets is an exciting area that is rapidly developing and is primed to revolutionize genome analysis. We embrace the potential that deep learning holds for understanding genome biology, and we encourage further advances in this area, extending to all aspects of genomics research.
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IEEE AccessPregnancy complications significantly impact maternal and fetal health, requiring accurate and timely diagnostic methods for life-saving interventions.
Ariana Deyaneira Jimenez-Narvaez +6 more
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Aging and Cancer, EarlyView.Cancer treatment is associated with measurable acceleration of biological aging across epigenetic, telomere, senescence, and immune biomarkers. However, biomarker validation and interventional strategies remain limited, especially in hematologic malignancies, underscoring the need for standardized multi‐omic aging assessments and adequately powered ...
Moataz Ellithi +3 more
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Breast Cancer ResearchBackground There is an ongoing need for first-line (1L) chemotherapies for metastatic HER2-negative breast cancer. This study evaluated the efficacy and safety profile of the dose-expansion part 6 of Study 114 in patients receiving E7389-LF as a 1L ...
Hiroshi Ishiguro +16 more
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New Directions for Adult and Continuing Education, EarlyView.ABSTRACT Advancing artificial intelligence (AI) has transformed learning and work, yet higher education and professional development programs have not systematically equipped learners for AI‐prevalent environments. This lack of preparation creates uncertainty regarding control, responsibility, trust, and accountability.
Moon‐Heum Cho, Jerusalem Merkebu
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Deep reinforcement learning: Bridging learning and control in intelligent systems
Deep Reinforcement Learning (DRL) has gained popularity as a new approach in artificial intelligence that successfully integrates representation learning through Deep Learning with decision making in Reinforcement Learning.
Şahin, C. B. +8 more
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Scientific ReportsAccessible mammography datasets and innovative machine learning techniques are at the forefront of computer-aided breast cancer diagnosis. However, the opacity surrounding private datasets and the unclear methodology behind the selection of subset images
Ling Liao, Eva M. Aagaard
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Experimental and numerical study of the acoustic performance of a novel composite core thermal break
Case Studies in Construction MaterialsThis study assesses the acoustic performance of a novel thermal break system with composite cores through both experimental testing and Finite element (FE) analysis. The research explores the effects of varying the thicknesses of key materials, including
Tran-Van Han +4 more
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RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu +21 more
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Deep Internal Learning: Deep learning from a single input
IEEE Signal Processing MagazineDeep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data is scarce and diversity is large on the one hand, and on the other, there is a ...
Tom Tirer +3 more
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