Results 201 to 210 of about 2,877,467 (378)

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

PREDICTIVE ESTIMATION OF A COVARIANCE MATRIX AND ITS STRUCTURAL PARAMETERS

, 2017
Haruhiko Ogasawara
openalex   +2 more sources

Penerapan Diagram Kendali Multivariate Exponentially Weighted Moving Covariance Matrix (MEWMC) pada Pengendalian Kualitas Proses Produksi Air di PDAM Surya Sembada Kota Surabaya

, 2021
Yani Trimardiani, Muhammad Akbar, Wibawati Wibawati   +2 more
openalex   +2 more sources

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

An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis. [PDF]

Front Neuroinform, 2021
Al-Hadeethi H, Abdulla S, Diykh M, Deo RC, Green JH.   +4 more
europepmc   +1 more source

Advancing Efficient Error Reduction in DNA Data Storage Systems with Deep Learning‐Based Denoising Models

Advanced Intelligent Discovery, EarlyView.
Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo, Anshula Tandon, Nguyen Kim Uyen, Quoc Nam Le, Jee‐Hyong Lee, Sung Ha Park   +5 more
wiley   +1 more source

An Enhanced Smoothed L₀-Norm Direction of Arrival Estimation Method Using Covariance Matrix. [PDF]

Sensors (Basel), 2021
Paik JW, Lee JH, Hong W.
europepmc   +1 more source

A Study on Signal Estimation of Modified Beamformer Method using Perturbation Covariance Matrix

, 2017
Kwan-Hyeong Lee, Tae‐Joon Cho
openalex   +2 more sources

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

An Orthogonally Equivariant Estimator of the Covariance Matrix in High Dimensions and for Small Sample Sizes. [PDF]

J Stat Plan Inference, 2021
Banerjee S, Monni S.
europepmc   +1 more source