Results 181 to 190 of about 220,110 (307)

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients. [PDF]

Comput Biol Med
Oprisan A, Oprisan SA.
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

Quantitative Evaluation of Kidney and Gallbladder Stones by Texture Analysis Using Gray Level Co-Occurrence Matrix Based on Diagnostic Ultrasound Images. [PDF]

J Clin Med
Kim M, Kim K, Jeong HW, Lee Y.
europepmc   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Evaluating machine learning models for post-surgery treatment response assessment in glioblastoma multiforme: a comparative study of gray level co-occurrence matrix (GLCM), curvelet, and combined radiomics features selected by multiple algorithms. [PDF]

BMC Med Imaging
Alibabaei S, Yousefipour M, Rahmani M, Raminfard S, Tahmasbi M.   +4 more
europepmc   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

An Effective Computational Method for Predicting Self-Interacting Proteins Based on VGGNet Convolutional Neural Network and Gray-Level Co-occurrence Matrix. [PDF]

Evol Bioinform Online
Chu DH, An JY, Nie XM.
europepmc   +1 more source

Clinically Informed Intelligent Classification of Ovarian Cancer Cells by Label‐Free Holographic Imaging Flow Cytometry

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone, Beatrice Cavina, Daniele Gaetano Sirico, Martina Mugnano, Vittorio Bianco, Lisa Miccio, Anna Myriam Perrone, Anna Maria Porcelli, Giuseppe Gasparre, Ivana Kurelac, Pasquale Memmolo, Pietro Ferraro   +11 more
wiley   +1 more source

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer. [PDF]

World J Gastrointest Surg
Yang KF, Li SJ, Xu J, Zheng YB.
europepmc   +1 more source