Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction
Abstract The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are effective against genes that are critical for disease are difficult to identify. Following a disease-centric approach, there is a need to id...
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BMC
2024-12-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-06009-9 |
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| author | Y.-h Taguchi Turki Turki |
| author_facet | Y.-h Taguchi Turki Turki |
| author_sort | Y.-h Taguchi |
| collection | DOAJ |
| description | Abstract The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are effective against genes that are critical for disease are difficult to identify. Following a disease-centric approach, there is a need to identify genes critical to disease function and find drugs that are effective against them. By contrast, following a drug-centric approach comprises identifying the genes targeted by drugs, and then the diseases in which the identified genes are critical. Both of these processes are complex. Using a gene-centric approach, whereby we identify genes that are effective against the disease and can be targeted by drugs, is much easier. However, how such sets of genes can be identified without specifying either the target diseases or drugs is not known. In this study, a novel artificial intelligence-based approach that employs unsupervised methods and identifies genes without specifying neither diseases nor drugs is presented. To evaluate its feasibility, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to perform drug repositioning from protein-protein interactions (PPI) without any other information. Proteins selected by TD-based unsupervised FE include many genes related to cancers, as well as drugs that target the selected proteins. Thus, we were able to identify cancer drugs using only PPI. Because the selected proteins had more interactions, we replaced the selected proteins with hub proteins and found that hub proteins themselves could be used for drug repositioning. In contrast to hub proteins, which can only identify cancer drugs, TD-based unsupervised FE enables the identification of drugs for other diseases. In addition, TD-based unsupervised FE can be used to identify drugs that are effective in in vivo experiments, which is difficult when hub proteins are used. In conclusion, TD-based unsupervised FE is a useful tool for drug repositioning using only PPI without other information. |
| format | Article |
| id | doaj-art-3cf39f21820f44e39a18058c195525d3 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-3cf39f21820f44e39a18058c195525d32024-12-22T12:51:21ZengBMCBMC Bioinformatics1471-21052024-12-0125112410.1186/s12859-024-06009-9Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interactionY.-h Taguchi0Turki Turki1Department of Physics, Chuo UniversityDepartment of Computer Science, King Abdulaziz UniversityAbstract The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are effective against genes that are critical for disease are difficult to identify. Following a disease-centric approach, there is a need to identify genes critical to disease function and find drugs that are effective against them. By contrast, following a drug-centric approach comprises identifying the genes targeted by drugs, and then the diseases in which the identified genes are critical. Both of these processes are complex. Using a gene-centric approach, whereby we identify genes that are effective against the disease and can be targeted by drugs, is much easier. However, how such sets of genes can be identified without specifying either the target diseases or drugs is not known. In this study, a novel artificial intelligence-based approach that employs unsupervised methods and identifies genes without specifying neither diseases nor drugs is presented. To evaluate its feasibility, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to perform drug repositioning from protein-protein interactions (PPI) without any other information. Proteins selected by TD-based unsupervised FE include many genes related to cancers, as well as drugs that target the selected proteins. Thus, we were able to identify cancer drugs using only PPI. Because the selected proteins had more interactions, we replaced the selected proteins with hub proteins and found that hub proteins themselves could be used for drug repositioning. In contrast to hub proteins, which can only identify cancer drugs, TD-based unsupervised FE enables the identification of drugs for other diseases. In addition, TD-based unsupervised FE can be used to identify drugs that are effective in in vivo experiments, which is difficult when hub proteins are used. In conclusion, TD-based unsupervised FE is a useful tool for drug repositioning using only PPI without other information.https://doi.org/10.1186/s12859-024-06009-9Protein-protein interactionDrug repositioningArtificial intelligenceUnsupervised learningTensor decomposition |
| spellingShingle | Y.-h Taguchi Turki Turki Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction BMC Bioinformatics Protein-protein interaction Drug repositioning Artificial intelligence Unsupervised learning Tensor decomposition |
| title | Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction |
| title_full | Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction |
| title_fullStr | Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction |
| title_full_unstemmed | Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction |
| title_short | Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction |
| title_sort | novel artificial intelligence based identification of drug gene disease interaction using protein protein interaction |
| topic | Protein-protein interaction Drug repositioning Artificial intelligence Unsupervised learning Tensor decomposition |
| url | https://doi.org/10.1186/s12859-024-06009-9 |
| work_keys_str_mv | AT yhtaguchi novelartificialintelligencebasedidentificationofdruggenediseaseinteractionusingproteinproteininteraction AT turkiturki novelartificialintelligencebasedidentificationofdruggenediseaseinteractionusingproteinproteininteraction |