ReviewOmics : a journal of integrative biology2018
Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.
Review in Omics : a journal of integrative biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 3 of them syntheses that pooled it.
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
93 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Achieving Precision Medicine in Allergic Disease: Progress and Challenges.Frontiers in immunology · 2021Pooled it
- Deep learning in gastric tissue diseases: a systematic review.BMJ open gastroenterology · 2020Pooled it
- Operationalizing Precision Medicine in Drug Development: Predictive Biomarkers, Companion Diagnostics, and Regulatory Pathways.Clinical pharmacology and therapeutics · 2026Review
- Leveraging chatgpt' s advanced data analysis for forensic science research and applications.Forensic science, medicine, and pathology · 2026Article
- Applications of Metabolomics in the Development of Biomedicine and Biotechnology.Advances in experimental medicine and biology · 2026Review
- Artificial intelligence in optimizing antimicrobial therapy for gastro-renal disorders.Frontiers in cellular and infection microbiology · 2026Review
- Prediction of influenza virus infection based on deep learning and peripheral blood proteomics: A diagnostic study.Journal of advanced research · 2026Article
- Article
- Bacterial Proteomics and Antibiotic Resistance Identification: Is Single-Cell Analysis a Worthwhile Pursuit?Pathogens (Basel, Switzerland) · 2025Review
- Micro- and Nanoengineered Devices for Rapid Chemotaxonomic Profiling of Medicinal Plants.Nanomaterials (Basel, Switzerland) · 2025Review
- Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation.Molecular biotechnology · 2025Review
- AI in fungal drug development: opportunities, challenges, and future outlook.Frontiers in cellular and infection microbiology · 2025Review
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Struggling to Understand the NEC Spectrum-Could the Integration of Metabolomics, Clinical-Laboratory Data, and Other Emerging Technologies Help Diagnosis?Metabolites · 2024Review
- Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence-A Critical Review.Plants (Basel, Switzerland) · 2024Review
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
- Alternative splicing events as peripheral biomarkers for motor learning deficit caused by adverse prenatal environments.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Johann Gregor Mendel: the victory of statistics over human imagination.European journal of human genetics : EJHG · 2023Review
- Biomarkers of necrotizing enterocolitis in the era of machine learning and omics.Seminars in perinatology · 2023Review
33 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.
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.