ArticleFrontiers in genetics2022
A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Predicting 5-Year Mortality in Non-Small-Cell Lung Cancer Using the Korean Central Cancer Registry: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Systematic bibliometric and visualized analysis of global research trends, impact, emerging areas, and hotspots of artificial intelligence in personalized medicine.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Image generator for tabular data based on non-Euclidean metrics for CNN-based classification.PloS one · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Transformer-based representation learning for robust gene expression modeling and cancer prognosis.Scientific reports · 2025Article
- Classification of lung cancer severity using gene expression data based on deep learning.BMC medical informatics and decision making · 2025Article
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets.Cancers · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes.Cell biology and toxicology · 2024Article
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization.Journal of biological methods · 2024Review
- Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine.Cancer informatics · 2024Article
- A Relationship Prediction Method forJournal of fungi (Basel, Switzerland) · 2023Article
- Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication.BioData mining · 2023Article
- Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature.Biology · 2023Article
- Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine.Frontiers in pharmacology · 2022Article
- Artificial intelligence in oncology: Current status and possibilities (Review).Medicine internationalReview
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4 authors.
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Abstract
Lung cancer is the leading cause of the cancer deaths. Therefore, predicting the survival status of lung cancer patients is of great value. However, the existing methods mainly depend on statistical machine learning (ML) algorithms. Moreover, they are not appropriate for high-dimensionality genomics data, and deep learning (DL), with strong high-dimensional data learning capability, can be used to predict lung cancer survival using genomics data. The Cancer Genome Atlas (TCGA) is a great database that contains many kinds of genomics data for 33 cancer types. With this enormous amount of data, researchers can analyze key factors related to cancer therapy. This paper proposes a novel method to predict lung cancer long-term survival using gene expression data from TCGA. Firstly, we select the most relevant genes to the target problem by the supervised feature selection method called mutual information selector. Secondly, we propose a method to convert gene expression data into two kinds of images with KEGG BRITE and KEGG Pathway data incorporated, so that we could make good use of the convolutional neural network (CNN) model to learn high-level features. Afterwards, we design a CNN-based DL model and added two kinds of clinical data to improve the performance, so that we finally got a multimodal DL model. The generalized experiments results indicated that our method performed much better than the ML models and unimodal DL models. Furthermore, we conduct survival analysis and observe that our model could better divide the samples into high-risk and low-risk groups.
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