ReviewSingapore medical journal2023
How data science and AI-based technologies impact genomics.
Review in Singapore medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Leveraging AI for infectious disease modelling and public health decision making.BMC proceedings · 2026Article
- Genetic and Genomic Testing in Cardiovascular Disease: A Policy Statement From the American Heart Association.Circulation · 2025Review
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- "ItResearch ethics · 2025Article
- Alternative Splicing in Tumorigenesis and Cancer Therapy.Biomolecules · 2025Review
- Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.International journal of molecular sciences · 2025Article
- Population health management fit lifecycles in analytics.Frontiers in artificial intelligence · 2025Article
- Cutting-edge AI tools revolutionizing scientific research in life sciences.Biotechnologia · 2025Review
- Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.EClinicalMedicine · 2024Article
- Genetics and genomics: A frontier for clinicians.Singapore medical journal · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies.
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.