Results 201 to 210 of about 313,820 (280)

Comprehensive Evaluation of Advanced Imputation Methods for Proteomic Data Acquired via the Label-Free Approach. [PDF]

Int J Mol Sci
Wryk G, Gawor A, Bulska E.
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods. [PDF]

Sensors (Basel)
Sasu GV, Ciubotaru BI, Goga N, Vasilățeanu A.   +3 more
europepmc   +1 more source

Benchmarking imputation methods for discrete biological data

, 2023
Gendre M, Hauffe T, Pimiento C, Silvestro D.   +3 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

Challenge of missing data in observational studies: investigating cross-sectional imputation methods for assessing disease activity in axial spondyloarthritis. [PDF]

RMD Open
Georgiadis S, Pons M, Rasmussen S, Hetland ML, Linde L, di Giuseppe D, Michelsen B, Wallman JK, Olofsson T, Zavada J, Glintborg B, Loft AG, Codreanu C, Melim D, Almeida D, Provan SA, Kvien TK, Rantalaiho V, Peltomaa R, Gudbjornsson B, Palsson O, Rotariu O, MacDonald R, Rotar Z, Pirkmajer KP, Lass K, Iannone F, Ciurea A, Østergaard M, Ørnbjerg LM.   +29 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

Efficient imputation methods in case of measurement errors. [PDF]

Heliyon
Kumar A, Bhushan S, Shukla S, Bakr ME, Alshangiti AM, Balogun OS.   +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

Optimal class of memory type imputation methods for time-based surveys using EWMA statistics. [PDF]

Sci Rep
Kumar A, Bhushan S, Alomair AM.
europepmc   +1 more source