ArticleBiomolecules2020
Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- Current status and future direction of cancer research using artificial intelligence for clinical application.Cancer science · 2025Review
- Introduction of AI Technology for Objective Physical Function Assessment.Bioengineering (Basel, Switzerland) · 2024Review
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- The pathobiology of follicular lymphoma.Journal of clinical and experimental hematopathology : JCEH · 2023Article
- Review
- Analysis of super-enhancer using machine learning and its application to medical biology.Briefings in bioinformatics · 2023Review
- DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics.Scientific reports · 2023Article
- Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.Experimental hematology & oncology · 2022Review
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
- DeepFeature: feature selection in nonimage data using convolutional neural network.Briefings in bioinformatics · 2021Article
- Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research.Biomedicines · 2021Review
- Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics.Journal of personalized medicine · 2021Review
- Review
- A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.Biomolecules · 2021Review
- Application of Artificial Intelligence for Medical Research.Biomolecules · 2021Article
- Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology.Frontiers in oncology · 2021Review
- Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.Cancers · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second, each input variable's contribution to the prediction is usually difficult to interpret, owing to multiple nonlinear operations. Third, genetic data features sometimes have no innate structure. To alleviate these problems, we propose a modification to Diet Networks by adding element-wise input scaling. The original Diet Networks concept can considerably reduce the number of parameters of the fully-connected layers by taking the transposed data matrix as an input to its auxiliary network. The efficacy of the proposed architecture was evaluated on a binary classification task for lung cancer histology, that is, adenocarcinoma or squamous cell carcinoma, from a somatic mutation profile. The dataset consisted of 950 cases, and 5-fold cross-validation was performed for evaluating the model performance. The model achieved a prediction accuracy of around 80% and showed that our modification markedly stabilized the learning process. Also, latent representations acquired inside the model allowed us to interpret the relationship between somatic mutation sites for the prediction.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.