A hybrid multimodal machine learning model for Detecting Alzheimer's disease
J Sheng, Q Zhang, Q Zhang, L Wang, Z Yang… - Computers in Biology …, 2024 - Elsevier
Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has
limitations. Multimodal fusion of complementary biomarkers may improve diagnostic …
limitations. Multimodal fusion of complementary biomarkers may improve diagnostic …
Indirect estimation of pediatric reference interval via density graph deep embedded clustering
Establishing reference intervals (RIs) for pediatric patients is crucial in clinical decision-
making, and there is a critical gap of pediatric RIs in China. However, the direct sampling …
making, and there is a critical gap of pediatric RIs in China. However, the direct sampling …
Performance comparison between multi-level gene expression data in cancer subgroup classification
P Jeyananthan - Pathology-Research and Practice, 2024 - Elsevier
Cancer is a serious disease that can affect various parts of the body such as breast, colon,
lung or stomach. Each of these cancers has their own treatment dependent historical …
lung or stomach. Each of these cancers has their own treatment dependent historical …
Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets
This study presents a new machine learning algorithm, named Chemical Environment
Graph Neural Network (ChemGNN), designed to accelerate materials property prediction …
Graph Neural Network (ChemGNN), designed to accelerate materials property prediction …
Pathology steered stratification network for subtype identification in Alzheimer's disease
Background Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative
disorder characterized by three neurobiological factors beta‐amyloid, pathologic tau, and …
disorder characterized by three neurobiological factors beta‐amyloid, pathologic tau, and …
Clustering algorithm based on DINNSM and its application in gene expression data analysis
Z Li, C Song, J Yang, Z Jia, D Chen… - … and Health Care, 2024 - content.iospress.com
14 BACKGROUND: Selecting an appropriate similarity measurement method is crucial for
obtaining biologically meaningful 15 clustering modules. Commonly used measurement …
obtaining biologically meaningful 15 clustering modules. Commonly used measurement …
Clustering Algorithm Based on Dual-Index Nearest Neighbor Similarity Measure and Its Application in Gene Expression Data Analysis
J Yang, Z Jia, C Yan, L Tian, X Wu - 2023 - researchsquare.com
Background The critical step in analyzing gene expression data is to divide genes into co-
expression modules using module detection methods. Clustering algorithms are the most …
expression modules using module detection methods. Clustering algorithms are the most …