ReviewFrontiers in artificial intelligence2020
An Introductory Review of Deep Learning for Prediction Models With Big Data.
Review in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers.
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.
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Who cites it
99 citing papers in PubMed.
- Harnessing Genomic Information for Identifying the Geographic Origin of Five North American Tree Species in Trade.Evolutionary applications · 2026Article
- Physical Implementation of Optical Material-Based Neural Networks Processing Enabled by Long-Persistent Luminescence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Decoding shared pathogenic networks of oxidative stress in neuropsychiatric disorders to prioritize multi-target therapeutics from natural products.Cell biology and toxicology · 2026Article
- A Novel, Interpretable Machine Learning Model Predicts Furosemide Dosing After Congenital Cardiac Surgery.Pediatric cardiology · 2026Article
- Deep learning for incidence rate prediction and radiation risk assessment of solid tumors.Scientific reports · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Construction and validation of a machine learning-based prediction model for 48-hour reintubation risk in mechanically ventilated patients.Frontiers in medicine · 2026Article
- Article
- EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions.Scientific reports · 2025Article
- Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.Scientific reports · 2025Article
- A deep learning model trained on expressed transcripts across different tissue types reveals cell-type codon-optimization preferences.Nucleic acids research · 2025Article
- Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram-Applicable in Clinical Practice?-Critical Literature Review with Meta-Analysis.Healthcare (Basel, Switzerland) · 2025Review
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- Performance evaluation and comparative analysis of different machine learning algorithms in predicting postnatal care utilization: Evidence from the ethiopian demographic and health survey 2016.PLOS digital health · 2025Article
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability.Computational and structural biotechnology journal · 2024Article
- Complexity data science: A spin-off from digital twins.PNAS nexus · 2024Article
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- Neural Network-Based Filter Design for Compressive Raman Classification of Cells.Journal of chemical information and modeling · 2024Article
39 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible. On a downside, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. For this reason, we present in this paper an introductory review of deep learning approaches including Deep Feedforward Neural Networks (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks. These models form the major core architectures of deep learning models currently used and should belong in any data scientist's toolbox. Importantly, those core architectural building blocks can be composed flexibly-in an almost Lego-like manner-to build new application-specific network architectures. Hence, a basic understanding of these network architectures is important to be prepared for future developments in AI.
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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.