AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named 'TopGene' to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers.
Mar 15, 2021 · AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes. Publisher: IEEE.
Aug 8, 2022 · Deep learning algorithm can extract high- level features in unsupervised manner and predict cancer sub-types on future unseen data. High ...
Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can ...
In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto ...
In this project, we demonstrate how adversarial auto-encoder (AAE) model can be used to extract the features from high dimensional genetic (omics) data.
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AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-types and Extracting Biologically Relevant Genes. RK Mondol, ND Truong, M Reza, S ...
AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes. Raktim Kumar Mondol, Nhan Duy ...
Feb 28, 2023 · Mondol et al. Afexnet: An adversarial autoencoder for differentiating breast cancer sub-types and extracting biologically relevant genes ...
Feb 22, 2024 · AFExNet: An. Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant. Genes. IEEE/ACM ...