DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues
Abstract Background P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design o...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05400-2 |
| _version_ | 1827905200776544256 |
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| author | Roohallah Mahdi-Esferizi Behnaz Haji Molla Hoseyni Amir Mehrpanah Yazdan Golzade Ali Najafi Fatemeh Elahian Amin Zadeh Shirazi Guillermo A. Gomez Shahram Tahmasebian |
| author_facet | Roohallah Mahdi-Esferizi Behnaz Haji Molla Hoseyni Amir Mehrpanah Yazdan Golzade Ali Najafi Fatemeh Elahian Amin Zadeh Shirazi Guillermo A. Gomez Shahram Tahmasebian |
| author_sort | Roohallah Mahdi-Esferizi |
| collection | DOAJ |
| description | Abstract Background P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. Results We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). Conclusions Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients. |
| first_indexed | 2024-03-13T00:38:37Z |
| format | Article |
| id | doaj.art-49369452dfe14d789283eb694cc8121e |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-13T00:38:37Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-49369452dfe14d789283eb694cc8121e2023-07-09T11:26:37ZengBMCBMC Bioinformatics1471-21052023-07-0124112210.1186/s12859-023-05400-2DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissuesRoohallah Mahdi-Esferizi0Behnaz Haji Molla Hoseyni1Amir Mehrpanah2Yazdan Golzade3Ali Najafi4Fatemeh Elahian5Amin Zadeh Shirazi6Guillermo A. Gomez7Shahram Tahmasebian8Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical SciencesLaboratory of Systems Biology and Bioinformatics (LBB), University of TehranFaculty of Mathematics, Shahid Beheshti UniversityDepartment of Mathematics, Faculty of Basic Sciences, Iran University of Science and Technology,(IUST)Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical SciencesDepartment of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical SciencesCentre for Cancer Biology, SA Pathology and University of South AustraliaCentre for Cancer Biology, SA Pathology and University of South AustraliaCellular and Molecular Research Center, Basic Health Sciences Institute, Shahrekord University of Medical SciencesAbstract Background P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. Results We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). Conclusions Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.https://doi.org/10.1186/s12859-023-05400-2P4 medicineDeep learningGene expression matrixPrediction modelClassificationTumor |
| spellingShingle | Roohallah Mahdi-Esferizi Behnaz Haji Molla Hoseyni Amir Mehrpanah Yazdan Golzade Ali Najafi Fatemeh Elahian Amin Zadeh Shirazi Guillermo A. Gomez Shahram Tahmasebian DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues BMC Bioinformatics P4 medicine Deep learning Gene expression matrix Prediction model Classification Tumor |
| title | DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues |
| title_full | DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues |
| title_fullStr | DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues |
| title_full_unstemmed | DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues |
| title_short | DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues |
| title_sort | deep4med deep learning for p4 medicine to predict normal and cancer transcriptome in multiple human tissues |
| topic | P4 medicine Deep learning Gene expression matrix Prediction model Classification Tumor |
| url | https://doi.org/10.1186/s12859-023-05400-2 |
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