16 papers · ranked by Valyu relevance
Md Shafayet Hossain, Yi-Ping Phoebe Chen
Non-coding RNAs (ncRNAs), once considered genomic dark matter, are now established as key regulators of gene expression with widespread roles in cellular homeostasis and disease. In cancer, ncRNA expression is frequently and systematically dysregulated, and many of these molecules circulate in stable, protected form…
Nazanin Zahra Keshvari, Sara Asl Motaleb Nejad Sarkhab, Tara Shahmoradi, Mohammad Pourashory + 3 more
Omics datasets are inherently complex and involve an unmanageable number of factors, which render manual/traditional analyses unfruitful. Through the application of data-training protocols, the large number of parameters could be managed. Moreover, ML methods are generally categorized as either: Supervised ML: the…
Hadeer Kamal, Amira Y. Haikal, Mahmoud M. Saafan
Traffic collisions and congestion represent significant challenges within intelligent transportation systems (ITS). Consequently, a vehicular ad-hoc network (VANET) has been established. Numerous architectures have been incorporated into VANETs to manage the extensive data generated by vehicles. Collaboration with fog…
Wenbin Li, Rui Fan, Kun Xie, Jiehua Zhong + 12 more
EV-miRNAs are promising gastric cancer (GC) biomarkers for early diagnosis, yet their clinical application is limited by major detection challenges arising from the low abundance and high sequence homology of EV-miRNAs. Herein, we developed a dual-mode platform for sensitive EV-miRNA in situ profiling based on…
Yu Zhang, Hui Wang, Liping Zhang
Neonatal hyperbilirubinemia is common in early neonatal life, and delayed recognition of high-risk infants may result in bilirubin-induced neurological injury. Prediction models based on routinely available maternal, perinatal, neonatal, and standardized laboratory variables may support early risk stratification as an…
Xiaoyue Hu, Yuhao Ma, Ruixing Ming, Heping Zhang + 1 more
Identifying essential biomarkers remains a core challenge in elucidating the pathogenic mechanisms and achieving precise diagnosis of complex diseases. Deep neural networks offer immense predictive power, yet their lack of interpretability severely limits downstream biological insight. Here, we introduce DeepVaris, an…
Charles de Kergariou, Helmut Hauser, David Correa
Advances in novel materials science enable structures to function as intelligent machines by embedding memory and learning capabilities directly into materials. Our work introduces a physical adaptive material motor unit neural network, leveraging a new generation of controllable actuators composed of wood- and carbon…
Mathias Claeys, Neil Fam, Xiaojiao Xiao, Tejas Vyas + 10 more
Background Patient selection for transcatheter mitral valve edge-to-edge repair (MTEER) remains challenging, particularly in individuals with complex mitral valve anatomy. Conventional risk scores incorporate limited clinical variables and do not adequately account for detailed echocardiographic features, resulting in…
Qingli Chen, Mei Yuan, Wei Zhang, Le Tong
Workplace violence and sleep disorders are common among emergency department (ED) nurses, yet heterogeneity in violence exposure and its relationship with sleep disorders remains poorly understood. This nationwide cross-sectional study included 1540 Chinese ED nurses. Latent profile analysis identified violence…
Pao‐Yuan Huang, Heng‐Syu Lin, Cheng‐Yuan Peng, Ruei‐Hau Hsu + 1 more
Few prediction models have been specifically designed to evaluate the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B and cirrhosis. This study aimed to develop a machine learning-based prediction model to assess the risk of developing HCC in patients with hepatitis B virus (HBV)-related…
Daniel Rodrigo Serbena, Isabela Luiza Fraron Cieslack, Renan Cassiano Ratis, Débora Van Putten Chaves + 6 more
Purpose To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. Materials and methods A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five…
Zhongyan Du, Zhihao Xu, Chenxiao Yang, Yuanyuan Zhang + 2 more
Background Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease with progressive peribiliary inflammation and fibrosis. Disease-modifying therapies are lacking, and the cell-type-specific mechanisms linking genetic susceptibility to pathogenic immune states remain incompletely understood. Methods…
Kunyu Wang, Jon Paul Janet, Alessandro Tibo
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Lysanne Veerle Michels, Lucy Smith, Suzan Ghannam, Charles Gadd + 1 more
Introduction Artificial intelligence (AI) has rapidly advanced as a key analytical tool for processing complex datasets across disciplines, including environmental and health research. Meteorological data is increasingly used to understand and mitigate health risks, often linked to climate change. This rapid review…
Anni Rajamäki, Aleksi Reito, Mari Karsikas, Mika Niemeläinen + 1 more
Sir,-We thank Dr Riddle and Dr Dumenci for their insightful comments to our article “Predicting persistent pain after total knee arthroplasty using different machine learning algorithms” [1,2]. They highlighted a few important points from the methodology and proposed an alternative statistical approach to predict the…
Ah-Young Cho, Soo-Heang Eo, Mi-Jeong Jeon, Jae-Hoon Kim + 3 more
Background Entering clinical training, dental students must learn to read tooth-centered electronic dental records, but limited teaching time in patient-centered clinics can leave gaps in chart-reading literacy. Multimodal large language models (LLMs) that process dental record images may offer scalable educational…