Comparative Analysis of Four Matrix Dilution Methods for Eliminating IgM Paraprotein Interference in Prealbumin and Uric Acid Assays: Based on Two Case Reports. [PDF]
J Clin Lab AnalIgM paraproteins can cause significant interference in prealbumin (PA) and uric acid (UA) measurements, leading to falsely abnormal results. We systematically compared four dilution methods to eliminate this interference. Dilution with the PA reagent (containing PEG) effectively restored accurate PA results.
Chi X, Liu Z, Liu W, Jiang H, Zhang D.
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On Local Aggregation in Heterophilic Graphs
CoRR, 2021 Many recent works have studied the performance of Graph Neural Networks (GNNs) in the context of graph homophily - a label-dependent measure of connectivity. Traditional GNNs generate node embeddings by aggregating information from a node's neighbors in the graph.
Hesham Mostafa +2 more
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HP-GMN: Graph Memory Networks for Heterophilous Graphs
2022 IEEE International Conference on Data Mining (ICDM), 2022 Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption, where nodes with the same label are connected in graphs. Real-world problems bring us heterophily problems, where nodes with different labels are connected in graphs. MPNNs
Junjie Xu +3 more
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Label-Wise Graph Convolutional Network for Heterophilic Graphs
, 2021 Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications. However, most existing GNNs assume the graphs exhibit strong homophily in node labels, i.e., nodes with similar labels are connected in the graphs.
Enyan Dai +3 more
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Cadherin-6 controls neuronal migration during mouse neocortical development via an integrin-mediated pathway. [PDF]
FEBS JNeuronal migration plays essential roles in the establishment of mammalian brains, and its impairment causes severe developmental abnormalities and behavioral and cognitive deficits. Here, we report that cadherin‐6 (CDH6), an unusual cadherin molecule containing an RGD integrin‐binding motif in the first extracellular domain, controls the motility of ...
Hirota Y +7 more
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Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities. [PDF]
J Cell Mol MedABSTRACT Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational drug targets.
Zhang H +6 more
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Edge Directionality Improves Learning on Heterophilic Graphs
CoRR, 2023 Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data. However, while many real-world graphs are directed, the majority of today's GNN models discard this information altogether by simply making the graph undirected.
Emanuele Rossi 0001 +5 more
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Overlay Neural Networks for Heterophilous Graphs
, 2023 Graph Neural Networks (GNNs) have become increasingly popular for their ability to capture complex relationships within graphs by aggregating node neighbor information. However, in graphs exhibiting high levels of heterophily relevant distant nodes are missed during neighbor aggregation, thus limiting the GNN performance in tasks like node ...
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Comparison of Enzyme-Linked Immunosorbent Assay and Lateral Flow Assay to Measure Thyroid-Stimulating Hormone and Free T4 in Human Serum. [PDF]
J Clin Lab AnalTSH and fT4 measured by EIA and EFL were evaluated by two regression models: Deming and Passing Bablok. The Deming regression for TSH shows that the mean levels obtained by both methods do not present significant differences; however, the Passin‐Bablok regression identifies significant bias between both methods in the range of concentrations studied ...
Advíncula-Espino R +2 more
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On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
CoRR, 2022 We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity, and local assortativity, i.e., the tendency of linked nodes to have similar attributes.
Donald Loveland +5 more
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