ArticleBriefings in bioinformatics2016
The digital revolution in phenotyping.
Article in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 52 citations in OpenAlex.
- A scoping review of neurodegenerative manifestations in explainable digital phenotyping.NPJ Parkinson's disease · 2023Article
- Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR.Proceedings of machine learning research · 2022Article
- SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records.Proceedings. IEEE International Conference on Data Mining · 2021Article
- FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example.Journal of biomedical semantics · 2021Article
- Semantic similarity and machine learning with ontologies.Briefings in bioinformatics · 2021Review
- Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study.Journal of medical Internet research · 2021Article
- BioHackathon 2015: Semantics of data for life sciences and reproducible research.F1000Research · 2020Article
- Broadening horizons: the case for capturing function and the role of health informatics in its use.BMC public health · 2019Article
- Multitask learning and benchmarking with clinical time series data.Scientific data · 2019Article
- Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives.Frontiers in genetics · 2019Review
- Early-onset inflammatory bowel disease as a model disease to identify key regulators of immune homeostasis mechanisms.Immunological reviews · 2019Review
- A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling.Global transitions · 2019Article
- Expanding a radiology lexicon using contextual patterns in radiology reports.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine.JACC. Basic to translational science · 2017Review
- Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes.Scientific reports · 2017Article
- Opportunities and challenges of whole-genome and -exome sequencing.BMC genetics · 2017Review
- Clinical Research Informatics for Big Data and Precision Medicine.Yearbook of medical informatics · 2016Article
- An information model for computable cancer phenotypes.BMC medical informatics and decision making · 2016Article
- Novel bioinformatic developments for exome sequencing.Human genetics · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 1 institution.
Funding
Abstract
Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support 'bench to bedside' efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.
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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.