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iMVAN: integrative multimodal variational autoencoder and network fusion for biomarker identification and cancer subtype classification

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Abstract

Numerous research has been conducted to define the molecular and clinical aspects of various tumors from a multi-omics point of view. However, there are significant obstacles in integrating multi-omics via Machine Learning (ML) for biomarker identification and cancer subtype classification. In this research, iMVAN, an integrated Multimodal Variational Autoencoder and Network fusion, is presented for biomarker discovery and classification of cancer subtypes. First, MVAE is used on multi-omics data consisting of Copy Number Variation (CNV), mRNA, and Reverse Protein Phase Array (rppa) to discover the biomarkers associated with distinct cancer subtypes. Then, multi-omics integration is accomplished by fusing similarity networks. Ultimately, the MVAE latent data and network fusion are given to a Simplified Graph Convolutional Network (SGC) for categorizing cancer subtypes. The suggested study extracts the top 100 features, which are then submitted to the KEGG analysis and survival analysis test. The survival study identifies nine biomarkers, including AGT, CDH1, CALML5, ERBB2, CCND1, FZD6, BRAF, AR, and MSH6, as poor prognostic markers. In addition, the cancer subtypes are classified, and the performance is assessed. The experimental findings demonstrate that the iMVAN performed well, with an accuracy of 87%.

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Code Availability

The code for the iMVAN is available at the following link: https://github.com/Arwin94/iMVAN

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Acknowledgements

We are thankful to Dr. Vikas Sharma, an Assistant Professor in School of Mathematics, TIET, Patiala, for his thorough examination of the mathematical concepts presented in this work. His valuable suggestions and recommended changes have significantly enhanced the overall quality and rigor of the mathematical analysis.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Arwinder Dhillon, Ashima Singh & Vinod Kumar Bhalla

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  1. Arwinder Dhillon
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  2. Ashima Singh
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Correspondence to Arwinder Dhillon.

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Dhillon, A., Singh, A. & Bhalla, V.K. iMVAN: integrative multimodal variational autoencoder and network fusion for biomarker identification and cancer subtype classification. Appl Intell 53, 26672–26689 (2023). https://doi.org/10.1007/s10489-023-04936-3

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