Enhanced disease-disease association with information enriched disease representation
Objective: Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2023-03-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023391?viewType=HTML |
| _version_ | 1797853012277854208 |
|---|---|
| author | Karpaga Priyaa Kartheeswaran Arockia Xavier Annie Rayan Geetha Thekkumpurath Varrieth |
| author_facet | Karpaga Priyaa Kartheeswaran Arockia Xavier Annie Rayan Geetha Thekkumpurath Varrieth |
| author_sort | Karpaga Priyaa Kartheeswaran |
| collection | DOAJ |
| description | Objective:
Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation. Materials and
Methods:
An enhanced and integrated DDA framework is developed that integrates enriched literature-based with concept-based DDA representation. The literature component of the proposed framework uses PubMed abstracts and consists of improved neural network model that classifies DDAs for an enhanced literature-based DDA representation. Similarly, an ontology-based joint multi-source association embedding model is proposed in the ontology component using Disease Ontology (DO), UMLS, claims insurance, clinical notes etc. Results and
Discussion:
The obtained information rich disease representation is evaluated on different aspects of DDA datasets such as Gene, Variant, Gene Ontology (GO) and a human rated benchmark dataset. The DDA scores calculated using the proposed method achieved a high correlation mainly in gene-based dataset. The quantified scores also shown better correlation of 0.821, when evaluated on human rated 213 disease pairs. In addition, the generated disease representation is proved to have substantial effect on correlation of DDA scores for different categories of disease pairs.
Conclusion:
The enhanced context and semantic DDA framework provides an enriched disease representation, resulting in high correlated results with different DDA datasets. We have also presented the biological interpretation of disease pairs. The developed framework can also be used for deriving the strength of other biomedical associations. |
| first_indexed | 2024-04-09T19:43:43Z |
| format | Article |
| id | doaj.art-d0b474a007a3405f8e147ce324293a53 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-09T19:43:43Z |
| publishDate | 2023-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-d0b474a007a3405f8e147ce324293a532023-04-04T01:24:59ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-03-012058892893210.3934/mbe.2023391Enhanced disease-disease association with information enriched disease representationKarpaga Priyaa Kartheeswaran0Arockia Xavier Annie Rayan 1Geetha Thekkumpurath Varrieth 2Department of Computer Science and Engineering, CEG, Chennai, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, CEG, Chennai, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, CEG, Chennai, Tamil Nadu, IndiaObjective: Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation. Materials and Methods: An enhanced and integrated DDA framework is developed that integrates enriched literature-based with concept-based DDA representation. The literature component of the proposed framework uses PubMed abstracts and consists of improved neural network model that classifies DDAs for an enhanced literature-based DDA representation. Similarly, an ontology-based joint multi-source association embedding model is proposed in the ontology component using Disease Ontology (DO), UMLS, claims insurance, clinical notes etc. Results and Discussion: The obtained information rich disease representation is evaluated on different aspects of DDA datasets such as Gene, Variant, Gene Ontology (GO) and a human rated benchmark dataset. The DDA scores calculated using the proposed method achieved a high correlation mainly in gene-based dataset. The quantified scores also shown better correlation of 0.821, when evaluated on human rated 213 disease pairs. In addition, the generated disease representation is proved to have substantial effect on correlation of DDA scores for different categories of disease pairs. Conclusion: The enhanced context and semantic DDA framework provides an enriched disease representation, resulting in high correlated results with different DDA datasets. We have also presented the biological interpretation of disease pairs. The developed framework can also be used for deriving the strength of other biomedical associations.https://www.aimspress.com/article/doi/10.3934/mbe.2023391?viewType=HTMLdisease representationinformation integrationbiomedical literatureontologydda quantification |
| spellingShingle | Karpaga Priyaa Kartheeswaran Arockia Xavier Annie Rayan Geetha Thekkumpurath Varrieth Enhanced disease-disease association with information enriched disease representation Mathematical Biosciences and Engineering disease representation information integration biomedical literature ontology dda quantification |
| title | Enhanced disease-disease association with information enriched disease representation |
| title_full | Enhanced disease-disease association with information enriched disease representation |
| title_fullStr | Enhanced disease-disease association with information enriched disease representation |
| title_full_unstemmed | Enhanced disease-disease association with information enriched disease representation |
| title_short | Enhanced disease-disease association with information enriched disease representation |
| title_sort | enhanced disease disease association with information enriched disease representation |
| topic | disease representation information integration biomedical literature ontology dda quantification |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023391?viewType=HTML |
| work_keys_str_mv | AT karpagapriyaakartheeswaran enhanceddiseasediseaseassociationwithinformationenricheddiseaserepresentation AT arockiaxavierannierayan enhanceddiseasediseaseassociationwithinformationenricheddiseaserepresentation AT geethathekkumpurathvarrieth enhanceddiseasediseaseassociationwithinformationenricheddiseaserepresentation |