ReviewBriefings in bioinformatics2021
Deep learning in systems medicine.
Review 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 22 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in the diagnosis of maxillary sinus diseases: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Intelligent Medication Recommendation via Dynamic Prescription Modeling and Molecular Substructure Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- The Progress of Gout Prediction Models Based on Multi-source Data.Current rheumatology reviews · 2026Review
- A deep learning framework for automated dental segmentation and diagnostic report generation from cone-beam computed tomography.Head & face medicine · 2025Article
- Conditional universal differential equations capture population dynamics and interindividual variation in c-peptide production.NPJ systems biology and applications · 2025Article
- Exploring the molecular mechanism ofHeliyon · 2025Article
- Machine learning and multi-omics in precision medicine for ME/CFS.Journal of translational medicine · 2025Review
- Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy.Frontiers in oncology · 2025Article
- A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques.Frontiers in endocrinology · 2024Article
- Future implications of artificial intelligence in lung cancer screening: a systematic review.BJR open · 2024Article
- The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.Joint diseases and related surgery · 2023Article
- Article
- Improving therapeutic synergy score predictions with adverse effects using multi-task heterogeneous network learning.Briefings in bioinformatics · 2023Article
- Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks.Micromachines · 2022Article
- Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning.Contrast media & molecular imaging · 2022Article
- Immune subtype identification and multi-layer perceptron classifier construction for breast cancer.Frontiers in oncology · 2022Article
- Defining Blood Plasma and Serum Metabolome by GC-MS.Metabolites · 2021Review
- Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data.Biomedicines · 2021Review
- DeepFeature: feature selection in nonimage data using convolutional neural network.Briefings in bioinformatics · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.
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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.