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Integrated querying and version control of context-specific biological networks
(OXFORD UNIV PRESS, GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND, 2020)
Motivation: Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often ...
Consensus embedding for multiple networks: Computation and applications
(CAMBRIDGE UNIV PRESS32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473, 2022)
Machine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map the nodes into a lowdimensional space such that the ...
Intrinsic graph topological correlation for graph convolutional network propagation
(ELSEVIER, 2022)
Recently, Graph Convolutional Networks (GCNs) and their variants become popular to learn graph-related tasks. These tasks include link prediction, node classification, and node embedding, among many others. In the node ...
Developing a label propagation approach for cancer subtype classification problem
(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEYATATURK BULVARI NO 221, KAVAKLIDERE, ANKARA 00000, TURKEY, 2022)
Cancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various
subtypes with different clinical and biological implications. Based on these differences, ...
Node similarity-based graph convolution for link prediction in biological networks
(OXFORD UNIV PRESSGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND, 2021)
Background: Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing ...
Topological feature generation for link prediction in biological networks
(PEERJ INC, 2023)
Graph or network embedding is a powerful method for extracting missing or
potential information from interactions between nodes in biological networks. Graph
embedding methods learn representations of nodes and interactions ...
Fast computation of Katz index for efficient processing of link prediction queries
(SPRINGERVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS, 2021)
Network proximity computations are among the most common operations in various data mining applications, including link prediction and collaborative filtering. A common measure of network proximity is Katz index, which has ...