Results 171 to 180 of about 136,861 (303)

Generalizable morphological profiling of cells by interpretable unsupervised learning. [PDF]

Nat Commun
Murthy RS, Stassen SV, Siu DMD, Lo MCK, Yip GGK, Tsia KK.   +5 more
europepmc   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Unsupervised learning enables instance-level microservice fault detection using traces and resource metrics. [PDF]

Sci Rep
Peng Z, Weigang L, Hao H, Yuntao C, Xinyue Z, Junsheng W.   +5 more
europepmc   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

Advanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Ademir Nied, Laio Oriel Seman, Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon   +5 more
wiley   +1 more source

An Unsupervised Learning Approach for Multimodal Low Back Pain Stratification. [PDF]

Spine (Phila Pa 1976)
Kowlagi N, Heikkala E, Saarakkala S, Karppinen J, Tiulpin A.   +4 more
europepmc   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models. [PDF]

BMC Bioinformatics
Qureshi A, Wahid A, Qazi S, Shahzad MK, Kiani HM, Asif MDA.   +5 more
europepmc   +1 more source

Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry

Advanced Intelligent Discovery, EarlyView.
Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa, Erving Ximendes, Luís D. Carlos, Carlos D. S. Brites   +3 more
wiley   +1 more source

Multicenter Study Suggests Unsupervised Learning Derived From MRI Identifies Prognostic Subgroups in Prostate Cancer Patients After Prostatectomy. [PDF]

Radiol Res Pract
Hu G, Liu X, Chen Z, Zhou B, Gan H, Chen R, Zhou L, Gu Y, Liu X.   +8 more
europepmc   +1 more source