A Bidirectional-Based Spreading Activation Method for Human Diseases Relatedness Detection Using Disease Ontology

The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.

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Bidirectional Spreading Activation Method for Finding Human Diseases Relatedness Using Well-Formed Disease Ontology

International Journal of Computers in Clinical Practice ◽

10.4018/ijccp.2017010104 ◽

2017 ◽

Vol 2 (1) ◽

pp. 45-58 ◽

Cited By ~ 3

Author(s):

Said Fathalla ◽

Yaman M. Khalid Kannot

Keyword(s):

Domain Knowledge ◽

Human Diseases ◽

Spreading Activation ◽

Semantic Matching ◽

Medical Systems ◽

Biomedical Knowledge ◽

Disease Ontology ◽

Bidirectional Search ◽

Spreading Activation Algorithm ◽

Two Phases

The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.

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Detecting Human Diseases Relatedness

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2018070106 ◽

2018 ◽

Vol 14 (3) ◽

pp. 120-133 ◽

Cited By ~ 3

Author(s):

Said Fathalla

Keyword(s):

Domain Knowledge ◽

Web Applications ◽

Human Diseases ◽

Spreading Activation ◽

Semantic Matching ◽

Search Technique ◽

Bidirectional Search ◽

Spreading Activation Algorithm ◽

Two Phases

Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

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Detecting Human Diseases Relatedness

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch026 ◽

2020 ◽

pp. 471-486

Author(s):

Said Fathalla

Keyword(s):

Domain Knowledge ◽

Web Applications ◽

Human Diseases ◽

Spreading Activation ◽

Semantic Matching ◽

Bidirectional Search ◽

Knowledge Graphs ◽

Spreading Activation Algorithm ◽

Two Phases

Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

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Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data

Nucleic Acids Research ◽

10.1093/nar/gku1011 ◽

2014 ◽

Vol 43 (D1) ◽

pp. D1071-D1078 ◽

Cited By ~ 348

Author(s):

Warren A. Kibbe ◽

Cesar Arze ◽

Victor Felix ◽

Elvira Mitraka ◽

Evan Bolton ◽

...

Keyword(s):

Human Diseases ◽

Biomedical Knowledge ◽

Disease Ontology

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Experimental Amyloidosis Induced by Immune Complex

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100066978 ◽

1971 ◽

Vol 29 ◽

pp. 582-583

Author(s):

A. Kawaoi

Keyword(s):

Immune Complex ◽

Systemic Amyloidosis ◽

Human Diseases ◽

Experimental Amyloidosis ◽

Freund’S Complete Adjuvant ◽

Freund's Complete Adjuvant ◽

Immunological Approach ◽

Experimentally Induced

Numbers of immunological approach have been made to the amyloidosis through the variety of predisposing human diseases and the experimentally induced animals by the greater number of agents. The results suggest an important role of impaired immunity involving both humoral and cell-mediated aspects.Recently the author has succeeded in producing amyloidosis in the rabbits and mice by the injections of immune complex of heat denatured DNA.The aim of this report is to demonstrate the details of the ultrastructure of the amyloidosis induced by heterologous insoluble immune complex. Eleven of twelve mice, dd strain, subcutaneously injected twice a week with Freund's complete adjuvant and four of seven animals intraperitonially injected developed systemic amyloidosis two months later from the initial injections. The spleens were electron microscopically observed.

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