ArticleBriefings in bioinformatics2021
DeepFeature: feature selection in nonimage data using convolutional neural network.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- ImmuGT-ConRes: a consistency learning residual network for predicting pan-cancer immunotherapy response from gene expression data.NPJ precision oncology · 2026Article
- DeepCE: a deep learning framework for correlation-enhanced gene regulatory network inference in single-cell RNA sequencing data.Bioinformatics advances · 2026Article
- SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping.Computational and structural biotechnology journal · 2026Article
- Design of a Multi-Epitope Vaccine using β-barrel Outer Membrane Proteins Identified in Chlamydia trachomatis.The Journal of membrane biology · 2025Article
- A Multi-Task Ensemble Strategy for Gene Selection and Cancer Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Transforming tabular data into images via enhanced spatial relationships for CNN processing.Scientific reports · 2025Article
- Development and validation of an explainable machine learning-based prediction model for primary Kawasaki disease complicated with coronary artery aneurysms.Translational pediatrics · 2025Article
- Convolutional-LSTM approach for temporal catch hotspots (CATCH): an AI-driven model for spatiotemporal forecasting of fisheries catch probability densities.Biology methods & protocols · 2025Article
- Advances in AI and machine learning for predictive medicine.Journal of human genetics · 2024Review
- Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network.Plants (Basel, Switzerland) · 2024Article
- Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis.NPJ systems biology and applications · 2024Article
- Enhanced analysis of tabular data through Multi-representation DeepInsight.Scientific reports · 2024Article
- Novel applications of Convolutional Neural Networks in the age of Transformers.Scientific reports · 2024Article
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- Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra.Journal of the American Chemical Society · 2023Article
- An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.Biology · 2023Review
- scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests. Among these methods, deep neural networks are currently among the most advanced approaches and, in particular, convolutional neural networks (CNNs) have been shown to perform excellently for a variety of difficult tasks. Despite that applications of this type of networks to high-dimensional omics data and, most importantly, meaningful interpretation of the results returned from such models in a biomedical context remains an open problem. Here we present, an approach applying a CNN to nonimage data for feature selection. Our pipeline, DeepFeature, can both successfully transform omics data into a form that is optimal for fitting a CNN model and can also return sets of the most important genes used internally for computing predictions. Within the framework, the Snowfall compression algorithm is introduced to enable more elements in the fixed pixel framework, and region accumulation and element decoder is developed to find elements or genes from the class activation maps. In comparative tests for cancer type prediction task, DeepFeature simultaneously achieved superior predictive performance and better ability to discover key pathways and biological processes meaningful for this context. Capabilities offered by the proposed framework can enable the effective use of powerful deep learning methods to facilitate the discovery of causal mechanisms in high-dimensional biomedical data.
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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.