Results 201 to 210 of about 375,599 (291)

Identifying recurrent stone formers with machine learning: A single-centre observational study. [PDF]

BJUI Compass
Amado P, Fuster DG, Bargagli M, Obrist D, Burkhard F, Roth B, Clavica F, Zheng S.   +7 more
europepmc   +1 more source

The Challenge of Handling Structured Missingness in Integrated Data Sources

Advanced Intelligent Discovery, EarlyView.
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson, Marius Hofert, Eleni‐Rosalina Andrinopoulou, Niels Hagenbuch, Sarah McGough, Chris Harbron, Robin Mitra   +6 more
wiley   +1 more source

Assessing imputation techniques for missing data in small and multicollinear datasets: insights from craniofacial morphometry. [PDF]

BMC Med Res Methodol
Abdullah NA, Hariri F, Hisam MNF, Binti Hassan SF.   +3 more
europepmc   +1 more source

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Advanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah, Muhammad Zulqarnain Zeb, Muhammad Husnain Zeb, Babar Shabbir   +3 more
wiley   +1 more source

A reference panel for linkage disequilibrium and genotype imputation using whole-genome sequencing data from 2,680 participants across India. [PDF]

HGG Adv
Li Z, Zhao W, Zhou X, Leung YY, Schellenberg GD, Wang LS, Schönherr S, Forer L, Fuchsberger C, Dey S, Lee J, Smith JA, Dey AB, Kardia SLR.   +13 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

Multi-output learning for systematic missing value imputation in DNA methylation arrays. [PDF]

Bioinform Adv
Ma T, Nie J, Huang J, Zhang YB, Biernacka JM, Wang L.   +5 more
europepmc   +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

Benchmarking imputation strategies for missing time-series data in critical care using real-world-inspired scenarios. [PDF]

Sci Rep
Poette M, Mouysset S, Ruiz D, Pey V, Alliot JM, Minville V.   +5 more
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source