Dataset Information

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 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.

SUBMITTER: Mahdi-Esferizi R

PROVIDER: S-EPMC10320882 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Publications

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

Mahdi-Esferizi Roohallah R Haji Molla Hoseyni Behnaz B Mehrpanah Amir A Golzade Yazdan Y Najafi Ali A Elahian Fatemeh F Zadeh Shirazi Amin A Gomez Guillermo A GA Tahmasebian Shahram S

BMC bioinformatics 20230704 1

<h4>Background</h4>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.<h4>Results</h4>We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Tr ...[more]

PMID: 37403016

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Dataset Information

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

Background

Results

Conclusions

Publications

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

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