Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction
Developing a drug from scratch is a very long and expensive process that has a small probability of success. For this reason, pharmaceutical companies are devoting their efforts to find drugs that could be repositioned. When using a drug to treat a disease is necessary to consider what adverse reac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Cátedra UNESCO en Gestión de Información en las Organizaciones (La Habana)
2023-01-01
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| Series: | GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología |
| Subjects: | |
| Online Access: | https://gecontec.org/index.php/unesco/article/view/138 |
| _version_ | 1797803690277470208 |
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| author | María Elena García Ochagavía Yudivián Almeida Cruz Suilán Estévez Velarde Aimée Alonso Reina María Elena Ochagavía Roque |
| author_facet | María Elena García Ochagavía Yudivián Almeida Cruz Suilán Estévez Velarde Aimée Alonso Reina María Elena Ochagavía Roque |
| author_sort | María Elena García Ochagavía |
| collection | DOAJ |
| description |
Developing a drug from scratch is a very long and expensive process that has a small probability of success. For this reason, pharmaceutical companies are devoting their efforts to find drugs that could be repositioned. When using a drug to treat a disease is necessary to consider what adverse reactions it may cause, this is why the prediction of adverse reactions is highly related to drug repositioning. We propose the detection of overlapping communities over a biological network of chemicals, diseases and genes in order to find drug-disease pairs that could be used as basis for later drug repositioning and adverse reactions prediction analysis. Of the evaluated overlapping community detection algorithms, OSLOM got the best results, producing 724 communities from which was possible to extract 215944 drug-disease pairs not present in the analyzed graph. We illustrate the usefulness of this set through examples of associations between pairs found in the scientific literature.
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| first_indexed | 2024-03-13T05:25:43Z |
| format | Article |
| id | doaj.art-d7518b5ac57448748addce3561dfe5d5 |
| institution | Directory Open Access Journal |
| issn | 2255-5684 |
| language | English |
| last_indexed | 2024-03-13T05:25:43Z |
| publishDate | 2023-01-01 |
| publisher | Cátedra UNESCO en Gestión de Información en las Organizaciones (La Habana) |
| record_format | Article |
| series | GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología |
| spelling | doaj.art-d7518b5ac57448748addce3561dfe5d52023-06-15T08:07:08ZengCátedra UNESCO en Gestión de Información en las Organizaciones (La Habana)GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología2255-56842023-01-0172Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions PredictionMaría Elena García Ochagavía0Yudivián Almeida Cruz1Suilán Estévez Velarde2Aimée Alonso Reina3María Elena Ochagavía Roque4Universidad de La HabanaUniversidad de La HabanaUniversidad de La HabanaUniversidad de La HabanaCentro de Ingeniería Genética y Biotecnología Developing a drug from scratch is a very long and expensive process that has a small probability of success. For this reason, pharmaceutical companies are devoting their efforts to find drugs that could be repositioned. When using a drug to treat a disease is necessary to consider what adverse reactions it may cause, this is why the prediction of adverse reactions is highly related to drug repositioning. We propose the detection of overlapping communities over a biological network of chemicals, diseases and genes in order to find drug-disease pairs that could be used as basis for later drug repositioning and adverse reactions prediction analysis. Of the evaluated overlapping community detection algorithms, OSLOM got the best results, producing 724 communities from which was possible to extract 215944 drug-disease pairs not present in the analyzed graph. We illustrate the usefulness of this set through examples of associations between pairs found in the scientific literature. https://gecontec.org/index.php/unesco/article/view/138Drug repositioningadverse reactionsoverlapping community detectionbiological network |
| spellingShingle | María Elena García Ochagavía Yudivián Almeida Cruz Suilán Estévez Velarde Aimée Alonso Reina María Elena Ochagavía Roque Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología Drug repositioning adverse reactions overlapping community detection biological network |
| title | Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction |
| title_full | Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction |
| title_fullStr | Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction |
| title_full_unstemmed | Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction |
| title_short | Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction |
| title_sort | overlapping community detection on a graph of chemicals diseases and genes for drug repositioning and adverse reactions prediction |
| topic | Drug repositioning adverse reactions overlapping community detection biological network |
| url | https://gecontec.org/index.php/unesco/article/view/138 |
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