ArticleInternational journal of molecular sciences2021
Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
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- MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction.International journal of molecular sciences · 2024Article
- Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature.Biology · 2023Article
- Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.International journal of molecular sciences · 2023Review
- Progress of the "Molecular Informatics" Section in 2022.International journal of molecular sciences · 2023Article
- RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data.Journal of biological engineering · 2023Article
- Editorial: Using physical & genomics markers for smart therapy via expert systems with computer learning.Frontiers in genetics · 2023Article
- Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics".International journal of molecular sciences · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundLong non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy.
methodTo discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers.
resultsOur results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers.
conclusionThe proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers.
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