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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Netpro2vec: A Graph Embedding Framework for Biomedical Applications.

Ichcha Manipur, Mario Manzo, Ilaria Granata

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|May 7, 2021

Summary

This summary is machine-generated.

We introduce Netpro2vec, a novel neural embedding framework for complex biomedical networks. This method generates task-independent graph embeddings by analyzing probability distributions, improving data representation.

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Area of Science:

Computational biology
Network science
Machine learning

Background:

Structured data complexity in biomedical applications necessitates dimensionality reduction.
Current graph learning methods primarily focus on local node and edge neighborhoods.
Kernel graphs offer broader representations but rely on handcrafted features unsuitable for generalized models.

Purpose of the Study:

To bridge the gap between complex graph structures and generalized modeling.
To introduce Netpro2vec, a neural embedding framework for graph representation learning.
To generate task- and data-independent graph embeddings.

Main Methods:

Developed Netpro2vec, a neural embedding framework utilizing probability distribution representations of graphs.
Incorporated node descriptions beyond simple degree, including Transition Matrix and Node Distance Distribution.
Evaluated framework performance on synthetic and real-world biomedical network datasets.

Main Results:

Netpro2vec generates embeddings independent of specific tasks and data types.
The framework demonstrated effectiveness in a comprehensive experimental classification phase.
Performance was benchmarked against established competitor methods on diverse biomedical networks.

Conclusions:

Netpro2vec offers a robust approach to representing complex biomedical networks.
The framework's ability to learn from probability distributions enhances graph embedding generalization.
Netpro2vec provides a valuable tool for analyzing and classifying biomedical network data.