A new initiative on precision medicine.

By Francis S. Collins, Harold Varmus. 2015

4,564 citations recorded by the source corpus.

Subfields: Cancer Research, Oncology, Pulmonary and Respiratory Medicine

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In Cancer Research

In Oncology

In Pulmonary and Respiratory Medicine

Citations in the corpus

Federated learning of predictive models from federated Electronic Health Records (2018, background): “Especially under the Precision Medicine Initiative,[12] in the near future, these data could include individuals’ genome information, which is too sensitive to be shared.”
Machine Learning Model Interpretability for Precision Medicine (2016, background): “Precision medicine holds a great future in healthcare as it customizes medical care to an individual’s unique disease state [1].”
Deep learning for healthcare: review, opportunities and challenges (2018, neutral): “…for example, precision medicine attempts to ‘ensure that the right treatment is delivered to the right patient at the right time’ by taking into account several aspects of patient’s data, including variability in molecular traits, environment, electronic health records (EHRs) and lifestyle [1–3].”
Mobile technology and the digitization of healthcare. (2016, background): “On one hand, the strengths of such designs include an individualized approach to treatments, and to quantify disease trends with multiple repeated measures that were previously unidentifiable.(56) On the other hand, tracking minor variations may reveal sub-clinical changes that do not require interv”
A machine learning-based framework to identify type 2 diabetes through electronic health records (2016, background): “A significant number of research investigations have been devoted to it, notably by means of genome-wide association study (GWAS) and phenome-wide association study (PheWAS) in hope of detecting more associations between genotypes and phenotypes [3-10, 23-26, 36].”
Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects (2016, neutral): “In the context of these ambitious efforts, it is important to quantify the power of large sequencing projects to discover rare functional genetic variants 3 .”
Deep Learning in Medical Imaging: General Overview (2017, background): “The original concept of precision medicine involves the prevention and treatment strategies that consider individual variability (98) by assessing large sets of data, including patient information, medical imaging, and genomic sequences.”
Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults (2016, neutral): “Given the current public interest in precision medicine 33 , these ﬁndings have practical implications for sequencing-based screening of nascent malignancy or recurrence.”
Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model (2017, background): “There are three main reasons: (1) professional background and rich experience of pathologists are so difficult to inherit or innovate that primary-level hospitals and clinics suffer from the absence of skilled pathologists, (2) the tedious task is expensive and time-consuming, and (3) over fatigue o”