Predicting biomedical relationships using the knowledge and graph embedding cascade model.

08:00 EDT 13th June 2019 | BioPortfolio

Summary of "Predicting biomedical relationships using the knowledge and graph embedding cascade model."

Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within biochemical networks. Knowledge embedding models have been found to have value in detecting knowledge-based correlations among entities, but little effort has been made to apply them to networks of biochemical entities. This is because such networks tend to be unbalanced and sparse, and knowledge embedding models do not work well on them. However, to some extent, the shortcomings of knowledge embedding models can be compensated for if they are used in association with graph embedding. In this paper, we combine knowledge embedding and graph embedding to represent biochemical entities and their relations as dense and low-dimensional vectors. We build a cascade learning framework which incorporates semantic features from the knowledge embedding model, and graph features from the graph embedding model, to score the probability of linking. The proposed method performs noticeably better than the models with which it is compared. It predicted links and entities with an accuracy of 93%, and its average hits@10 score has an average of 8.6% absolute improvement compared with original knowledge embedding model, 1.1% to 9.7% absolute improvement compared with other knowledge and graph embedding algorithm. In addition, we designed a meta-path algorithm to detect path relations in the biomedical network. Case studies further verify the value of the proposed model in finding potential relationships between diseases, drugs, genes, treatments, etc. Amongst the findings of the proposed model are the suggestion that VDR (vitamin D receptor) may be linked to prostate cancer. This is backed by evidence from medical databases and published research, supporting the suggestion that our proposed model could be of value to biomedical researchers.


Journal Details

This article was published in the following journal.

Name: PloS one
ISSN: 1932-6203
Pages: e0218264


DeepDyve research library

PubMed Articles [12513 Associated PubMed Articles listed on BioPortfolio]

Stochastic Graphlet Embedding.

Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semistructured data as graphs where nodes correspond to primitives (par...

Learning a discriminant graph-based embedding with feature selection for image categorization.

Graph-based embedding methods are very useful for reducing the dimension of high-dimensional data and for extracting their relevant features. In this paper, we introduce a novel nonlinear method calle...

Joint sparse graph and flexible embedding for graph-based semi-supervised learning.

This letter introduces a framework for graph-based semi-supervised learning by estimating a flexible non-linear projection and its linear regression model. Unlike existing works, the proposed framewor...

Graph Convolutional Network Hashing.

Recently, graph-based hashing that learns similarity-preserving binary codes via an affinity graph has been extensively studied for large-scale image retrieval. However, most graph-based hashing metho...

Concept Embedding to Measure Semantic Relatedness for Biomedical Information Ontologies.

There have been many attempts to identify relationships among concepts corresponding to terms from biomedical information ontologies such as the Unified Medical Language System (UMLS). In particular, ...

Clinical Trials [3194 Associated Clinical Trials listed on BioPortfolio]

A Clinical Trial of Thread-embedding Therapy at Acupuncture Point for Simple Obesity

Thread-embedding Therapy has been used for treating Obesity in recent years. This research is aimed to observe the clinical effect of thread-embedding therapy in treating Simple Obesity. O...

Clinical Research on the Efficacy of Thread-embedding Acupuncture on Herniated Intervertebral Disc of Lumbar Spine

This clinical trial is designed to evaluate the efficacy and safety of thread-embedding acupuncture on lumbar herniated intervertebral disc (L-HIVD) by assessing pain, function, and qualit...

Suture Embedding Acupuncture for Chronic Low Back Pain

Chronic low back pain (cLBP) is a common public health issue, and it is one of the main causes of disability among adults of working age. Suture embedding acupuncture is one of the most of...

How Participants Perceive Biomedical Research in Pulmonology

The primary objective of this study is to determine how biomedical research is perceived by patients already participating in a pulmonology research project.

Physician Disclosures in the Real World of Conflicting Interests

This study seeks to ascertain the best way to inform patients about their physicians' conflicts of interest (COI) with industry. Currently, there is no institutional or national standard f...

Medical and Biotech [MESH] Definitions

The technique of placing cells or tissue in a supporting medium so that thin sections can be cut using a microtome. The medium can be paraffin wax (PARAFFIN EMBEDDING) or plastics (PLASTIC EMBEDDING) such as epoxy resins.

The interdisciplinary field concerned with the development and integration of behavioral and biomedical science, knowledge, and techniques relevant to health and illness and the application of this knowledge and these techniques to prevention, diagnosis, treatment, and rehabilitation.

A research and development program initiated by the NATIONAL LIBRARY OF MEDICINE to build knowledge sources for the purpose of aiding the development of systems that help health professionals retrieve and integrate biomedical information. The knowledge sources can be used to link disparate information systems to overcome retrieval problems caused by differences in terminology and the scattering of relevant information across many databases. The three knowledge sources are the Metathesaurus, the Semantic Network, and the Specialist Lexicon.

Application of principles and practices of engineering science to biomedical research and health care.

Evaluation of biomedical technology in relation to cost, efficacy, utilization, etc., and its future impact on social, ethical, and legal systems.

Quick Search


DeepDyve research library

Relevant Topic

Within medicine, nutrition (the study of food and the effect of its components on the body) has many different roles. Appropriate nutrition can help prevent certain diseases, or treat others. In critically ill patients, artificial feeding by tubes need t...

Searches Linking to this Article