Results 241 to 250 of about 30,503 (277)

Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration. [PDF]

Health Inf Sci Syst
Chen F, Peng W, Dai W, Wei S, Fu X, Liu L, Liu L.   +6 more
europepmc   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

Advanced Science, EarlyView.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv, Qianyang Wu, Jian Liu, Binlu Yang, Yuanhao Li, Junlin Xu, Yajie Meng, Leyi Wei, Zilong Zhang, Quan Zou, Xiulai Li, Feifei Cui   +11 more
wiley   +1 more source

Peroxidase‐Mimicking Nanozymes for Rapid Detection of Infectious Diseases

Advanced Science, EarlyView.
Peroxidase‐mimicking nanozymes (PMNs) have emerged as robust and versatile materials for rapid infectious disease diagnostics. This review highlights the rational design and controlled synthesis of PMNs, summarizes key biomarkers relevant to infectious diseases, examines their integration into diverse rapid detection platforms, and highlights ...
Shikuan Shao, Victor Fernandez‐Gonzalez, Xiaochuan Wang, Blake Pylipow, Claudia Herrera, Xiaohu Xia   +5 more
wiley   +1 more source

CMCL-DDI: Pharmacophore-aware cross-view contrastive learning for drug-drug interaction prediction. [PDF]

PLoS One
Han Y, Du L.
europepmc   +1 more source

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Relation-aware Graph Contrastive Learning

Parallel Processing Letters, 2023
Over the past few years, graph contrastive learning (GCL) has gained great success in processing unlabeled graph-structured data, but most of the existing GCL methods are based on instance discrimination task which typically learns representations by minimizing the distance between two versions of the same instance.
Bingshi Li, Jin Li, Yang-Geng Fu
openaire   +2 more sources

Graph prototypical contrastive learning

Information Sciences, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peng, Meixin, Juan, Xin, Li, Zhanshan
openaire   +1 more source

Probability graph complementation contrastive learning

Neural Networks
Graph Neural Network (GNN) has achieved remarkable progress in the field of graph representation learning. The most prominent characteristic, propagating features along the edges, degrades its performance in most heterophilic graphs. Certain researches make attempts to construct KNN graph to improve the graph homophily.
Wenhao Jiang, Yuebin Bai
openaire   +2 more sources

Multimodal Graph Meta Contrastive Learning

Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021
In recent years, graph contrastive learning has achieved promising node classification accuracy using graph neural networks (GNNs), which can learn representations in an unsupervised manner. However, such representations cannot be generalized to unseen novel classes with only few-shot labeled samples in spite of exhibiting good performance on seen ...
Feng Zhao, Donglin Wang
openaire   +1 more source

Integrative oncology: Addressing the global challenges of cancer prevention and treatment

Ca-A Cancer Journal for Clinicians, 2022
Jun J Mao,, Msce, Lorenzo Cohen, Karen M Mustian   +2 more
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