AMU: Using mRNA Embedding in Self-Attention Network to Predict Melanoma Immune Checkpoint Inhibitor Response

Abstract

Background: To precisely predict drug response and avoid unnecessary treatment have been urgent needs to be resolved in the age of melanoma immunotherapy. Deep learning model is a powerful instrument to predict drug response. Simultaneously extracting the function and expression data characteristics of mRNA may help to improve the prediction performance of the model. Methods: We designed a deep learning model named AMU with self-attention structure which were fed with the mRNA expression values for predicting melanoma immune checkpoint inhibitor clinical responses. Results: Comparing with SVM, Random Forest, AdaBoost, XGBoost and the classic convolutional network, AMU showed the preferred performance with the AUC of 0.941 and mAP of 0.960 in validation dataset and AUC of 0.672, mAP of 0.800 in testing dataset, respectively. In model interpretation work, TNF-TNFRSF1A pathway were indicated as a key pathway to influence melanoma immunotherapy responses. Further, gene features extracted from embedding layer and calculated by t-SNE algorithm, showed a local similarity with Functional Protein Association Network (STRING, https://cn.string-db.org/), AMU could predict gene functions and interactions simultaneously. Conclusions: Deep learning model built with self-attention structure has strong power to process mRNA expression data and gene vector representation is a promising work in biomedical field.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

GSE78220, GSE91061, GSE165278 from GEO Datasets PMID:31792460

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

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Data Availability

All data produced in the present study are available upon reasonable request to the authors All data produced in the present work are contained in the manuscript All data produced are available online at https://aistudio.baidu.com/aistudio/projectdetail/4298990

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