Journal article
Similar Disease Prediction With Heterogeneous Disease Information Networks
IEEE TRANSACTIONS ON NANOBIOSCIENCE, v 19(3), pp 571-578
Jul 2020
PMID: 32603299
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Studying the similarity of diseases can help us to explore the pathological characteristics of complex diseases, and help provide reliable reference information for inferring the relationship between new diseases and known diseases, so as to develop effective treatment plans. To obtain the similarity of the disease, most previous methods either use a single similarity metric such as semantic score, functional score from single data source, or utilize weighting coefficients to simply combine multiple metrics with different dimensions. In this paper, we proposes a method to predict the similarity of diseases by node representation learning. We first integrate the semantic score and topological score between diseases by combining multiple data sources. Then for each disease, its integrated scores with all other diseases are utilized to map it into a vector of the same spatial dimension, and the vectors are used to measure and comprehensively analyze the similarity between diseases. Lastly, we conduct comparative experiment based on benchmark set and other disease nodes outside the benchmark set. Using the statistics such as average, variance, and coefficient of variation in the benchmark set to evaluate multiple methods demonstrates the effectiveness of our approach in the prediction of similar diseases.
Metrics
Details
- Title
- Similar Disease Prediction With Heterogeneous Disease Information Networks
- Publication Details
- IEEE TRANSACTIONS ON NANOBIOSCIENCE, v 19(3), pp 571-578
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC; PISCATAWAY
- Number of pages
- 0
- Grant note
- This work was supported in part by NSFC under Grant 61873288, in part by the National Science Foundation (NSF) under Grant 1815256 and Grant 1744661, and in part by the Hunan Key Laboratory for Internet of Things in Electricity under Grant 2019TP1016. This article was presented in part at the 2019 IEEE International Conference on Bioinformatics and Biomedicine.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000545423500027
- Scopus ID
- 2-s2.0-85087432061
- Other Identifier
- 991021860767804721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Nanoscience & Nanotechnology