SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization
نویسندگان
چکیده
منابع مشابه
Graph Hybrid Summarization
One solution to process and analysis of massive graphs is summarization. Generating a high quality summary is the main challenge of graph summarization. In the aims of generating a summary with a better quality for a given attributed graph, both structural and attribute similarities must be considered. There are two measures named density and entropy to evaluate the quality of structural and at...
متن کاملRevealing Biological Modules via Graph Summarization
The division of a protein interaction network into biologically meaningful modules can aid with automated detection of protein complexes and prediction of biological processes and can uncover the global organization of the cell. We propose the use of a graph summarization (GS) technique, based on graph compression, to cluster protein interaction graphs into biologically relevant modules. The me...
متن کاملGraph-based models for multi-document summarization
University of Ljubljana Faculty of Computer and Information Science Ercan Canhasi Graph-based models for multi-document summarization is thesis is about automatic document summarization, with experimental results on general, query, update and comparative multi-document summarization (MDS). We describe prior work and our own improvements on some important aspects of a summarization system, incl...
متن کاملMulti-document Summarization with Graph Metrics
In this paper we introduce two systems RSumm and CNSumm, which are multi-document summarizers based on the adaptation of the singledocument relationship map and complex network methods, which represent texts as graphs and select sentences to compose the summary by using different graph traversing strategies and complex networks measures.
متن کاملGraph-based Neural Multi-Document Summarization
We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2021
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btab207