The path to personalized medicine.

By Joanne M. Meyer, Geoffrey S. Ginsburg. 2002

1,031 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “All these aspects together are crucial for applicability in medicine generally, and for future personalized medicine speciﬁcally (Hamburg and Collins, 2010).”
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015, background): “Recently, there is a growing interest in applying deep learning techniques to computational phenotyping [33] due to the increasing availability of the Electronic Healthcare Records (EHR) and the need for Personalized Healthcare [4, 17].”
Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment (2014, neutral): “2014–2:14pm] [1–41] //blrnas3/cenpro/ApplicationFiles/Journals/SAGE/3B2/SMMJ/Vol00000/140066/APPFile/SG-SMMJ140066.”
European Position Paper on Rhinosinusitis and Nasal Polyps 2020. (2020, background): “aureus is most closely associated with a subpopulation of CRS patients, specifically severe CRSwNP and asthma patients exhibiting high tissue eosinophilia((160)).”
Deep into the Brain: Artificial Intelligence in Stroke Imaging (2017, background): “Precision medicine, defined as tailored diagnosis and therapy for an individual patient, is an important emerging concept in future medicine.(5) The recent explosion of clinical, biological (genomic and immunological), and imaging data is creating a path for such personalized medicine.”
The Personalized Advantage Index: Translating Research on Prediction into Individualized Treatment Recommendations. A Demonstration (2014, background): “The call for an increased focus on ‘‘personalized medicine [1] ’’ is being met by efforts across medical fields to identify predictors of treatment response [2].”
Small Sample Size Solutions (2020, background): “This is especially true in an era in which medicine is increasingly personalized (Hamburg & Collins, 2010; Ng, Murray, Levy, & Venter, 2009).”
Learning Optimal Individualized Treatment Rules from Electronic Health Record Data (2016, background): “During the past decade, medical research is experiencing a paradigm shift from “one-size-ﬁts-all” strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed [1], [2].”
New Scanner Data for Brand Marketers: How Neuroscience Can Help Better Understand Differences in Brand Preferences (2012, background): “To some critics, neuroscience does not informmodels of behavior; it merely provides a pseudo-scientific veneer for unwarranted claims (Gul & Pesendorfer, 2008; Harrison, 2008).”
Alzheimer’s disease: pathogenesis, diagnostics, and therapeutics (2019, background): “The US Food and Drug Administration (FDA) and National Institutes of Health are supporting the concept of personalized nano-medicine, which may usher in a revolution in drug delivery across the BBB, contributing to better health care and more opportunities to combat CNS diseases.(94) The success of ”
Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules (2017, background): “It is widely recognized that best possible clinical care is tailored to individual patient characteristics (Sox et al., 2008; Hamburg and Collins, 2010; Collins and Varmus, 2015), including subjective factors like patient personal preference (Edwards and Elwyn, 2009).”
Embracing the unknown: disentangling the complexities of the soil microbiome (2017, background): “99) will be required, and simply knowing what microorganisms are found, or introduced into, a given soil will probably be of limited use if the context is ignored.”