ReviewExpert review of molecular diagnostics2018
Informatics and machine learning to define the phenotype.
Review in Expert review of molecular diagnostics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Machine learning-based precision subtyping and risk prediction in sepsis: a retrospective analysis using MIMIC-IV database.BMC infectious diseases · 2026Article
- Unveiling Phenotypic Heterogeneity in Coronary Spastic Angina Through Multidimensional Risk Profiling: The FUJI-SPASM Study.JACC. Advances · 2026Article
- Progress and challenges in sporadic late-onset cerebellar ataxias.Nature reviews. Neurology · 2025Review
- All-cause and cause-specific mortality in respiratory symptom clusters: a population-based multicohort study.Respiratory research · 2025Article
- Methods for identifying health status from routinely collected health data: An overview.Integrative medicine research · 2025Article
- Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine-based approach in diverse healthcare systems.Clinical and translational science · 2025Article
- Phenotyping people with a history of injecting drug use within electronic medical records using an interactive machine learning approach.NPJ digital medicine · 2024Article
- A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems.medRxiv : the preprint server for health sciences · 2023Article
- Blood-Based Biomarkers for Managing Workload in Athletes: Perspectives for Research on Emerging Biomarkers.Sports medicine (Auckland, N.Z.) · 2023Review
- Mechanobiology as a tool for addressing the genotype-to-phenotype problem in microbiology.Biophysics reviews · 2023Review
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 2023Article
- Computational phenotyping of obstructive airway diseases: protocol for a systematic review.Systematic reviews · 2022Article
- What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.Human genetics · 2022Review
- EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.Nucleic acids research · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Identifying primary care datasets and perspectives on their secondary use: a survey of Australian data users and custodians.BMC medical informatics and decision making · 2022Article
- Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.Journal of clinical monitoring and computing · 2022Article
- A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk.Scientific reports · 2022Article
- Short Sequence Chinese-English Machine Translation Based on Generative Adversarial Networks of Emotion.Computational intelligence and neuroscience · 2022Article
- Deploying machine learning with messy, real world data in low- and middle-income countries: Developing a global health use case.Frontiers in big data · 2022Article
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
Abstract
introductionFor the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.
Indexed as
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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.