ReviewGenomics, proteomics & bioinformatics2018
Deep Learning and Its Applications in Biomedicine.
Review in Genomics, proteomics & bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
155 citing papers in PubMed, 618 citations in OpenAlex.
- Deep Learning Based on Automated Breast Volume Scanner Images for the Diagnosis of Breast Lesions: A Multicenter Diagnostic Study.International journal of medical sciences · 2025Trial
- Enhanced ovarian cancer diagnosis using deep learning on pelvic ultrasound with integrated clinical data: retrospective multicenter study.Journal of gynecologic oncology · 2026Article
- Machine learning-based predictive model for postoperative delirium of elderly patients with coronary heart disease undergoing non-cardiac surgery: a retrospective cohort study.Frontiers in psychiatry · 2026Article
- Research on the construction of prediction model for depressive symptom in the second and third trimester of pregnancy based on artificial neural network.Frontiers in psychiatry · 2026Article
- Lipid-based Nanoparticles as Drug Delivery System for Modern Therapeutics.Pharmaceutical nanotechnology · 2026Review
- KoMethylNet: a novel epigenetic clock based on neural network analysis of DNA methylation data and epigenetic age acceleration in a Korean population.BMC medicine · 2025Article
- Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy.Nature communications · 2025Article
- From Molecules to Medicines: The Role of AI-Driven Drug Discovery Against Alzheimer's Disease and Other Neurological Disorders.Pharmaceuticals (Basel, Switzerland) · 2025Review
- CAPI-Detect: machine learning in capillaroscopy reveals new variables influencing diagnosis.Rheumatology (Oxford, England) · 2025Article
- AI-Driven TENGs for Self-Powered Smart Sensors and Intelligent Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.Scientific reports · 2025Article
- Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.BMC medical imaging · 2025Article
- Reprogrammed Plant Metabolism During Viral Infections: Mechanisms, Pathways and Implications.Molecular plant pathology · 2025Review
- Optimizing gene selection and module identification via ontology-based scoring and deep learning.Bioinformatics advances · 2025Article
- Tendon regeneration deserves better: focused review onFrontiers in bioengineering and biotechnology · 2025Review
- Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification.Computational and structural biotechnology journal · 2025Article
- Transcriptomic analysis reveals potential crosstalk genes and immune relationship between triple-negative breast cancer and depression.Discover oncology · 2024Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- Enhancing genomic disorder prediction through Feynman Concordance and Interpolated Nearest Centroid techniques.Scientific reports · 2024Article
- Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review.Sensors (Basel, Switzerland) · 2024Review
95 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
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.
Indexed as
Identifiers
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.