Predictive, personalized, preventive, participatory (P4) cancer medicine

By Leroy Hood, S. Friend. 2011

786 citations recorded by the source corpus.

Subfields: Cancer Research, Genetics, Molecular Biology

How researchers see this paper

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

In Cancer Research

In Genetics

In Molecular Biology

Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, positive): “Here, toolsets are urgently needed to support the bidirectional interaction with computational multiscale analysis and modelling to help to go towards the far-oﬀ goal of future medicine 7 (Hood and Friend, 2011; Tian et al., 2012).”
Deep Learning for Health Informatics (2017, background): “Further motivated by P4 (predictive, personalized, preventive, participatory) medicine [120], bioinformatics aims to predict and prevent diseases by involving patients in the development of more efficient and personalized treatments.”
Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions (2012, positive): “Patients and caregivers can use the rules to guide medical decisions [see Hood and Friend (2011), e.g.], while systems can use predictions to allocate resources [Vogenberg (2009)].”
Big Data Analytics in Healthcare (2015, methodology): “Developing a detailed model of a human being by combining physiological data and high-throughput “-omics” techniques has the potential to enhance our knowledge of disease states and help in the development of blood based diagnostic tools [20–22].”
The BioGRID interaction database: 2019 update (2018, methodology): “The biological network paradigm has been used to facilitate drug target selection, interpret drug resistance or offtarget effects, and forms the basis for targeted therapies and personalized medicine (14,15).”
Biobanks - A Source of Large Biological Data Sets: Open Problems and Future Challenges (2014, background): “The trend towards precision medicine (P4 Medicine: Predictive, Preventive, Participatory, Personalized) [6] has resulted in an explosion in the amount of generated biomedical data sets – in particular -omics data (e.”
From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare (2015, background): “Mining th ese numerous data sources per individual will help crea te predictive and actionable models [121] to better ta ilor therapeutic regimens thereby improving outcomes and quality of life.”