Quantifying factors for the success of stratified medicine.
about
Technologies for deriving primary tumor cells for use in personalized cancer therapyThe role of Abcb5 alleles in susceptibility to haloperidol-induced toxicity in mice and humansPrecision medicine in the age of big data: The present and future role of large-scale unbiased sequencing in drug discovery and development.Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.Can 'humanized' mice improve drug development in the 21st century?From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action.Aligning the economic value of companion diagnostics and stratified medicines.Lack of association of variants previously associated with anti-TNF medication response in rheumatoid arthritis patients: results from a homogeneous Greek populationPharmacogenomics of preterm birth prevention and treatment.A Quantitative Assessment of Factors Affecting the Technological Development and Adoption of Companion DiagnosticsPIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms.Therapeutic target database update 2014: a resource for targeted therapeutics.Patenting personalized medicines in the UK, Europe and USA.Individualizing prophylaxis in hemophilia: a review.The Reactive Species Interactome: Evolutionary Emergence, Biological Significance, and Opportunities for Redox Metabolomics and Personalized Medicine.Toward Precision Healthcare: Context and Mathematical ChallengesIdentifying molecular features that distinguish fluvastatin-sensitive breast tumor cells.Towards a balanced value business model for personalized medicine: an outlook.Clinical trial simulation to assist in COPD trial planning and design with a biomarker-based diagnostic: when to pull the trigger?Encapsulated Optically Responsive Cell Systems: Toward Smart Implants in Biomedicine.Anticancer drug development from traditional cytotoxic to targeted therapies: evidence of shorter drug research and development time, and shorter drug lag in Japan.Relationships of Ex-Vivo Drug Resistance Assay and Cytokine Production with Clinicopathological Features in the Primary Cell Culture of Thai Ovarian and Fallopian Tube Cancer PatientsMolecular biomarkers in clinical development: what could we learn from the clinical trial registry?[Personalised medicine. Aims and challenges].Stratified medicine: a call for action
P2860
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P2860
Quantifying factors for the success of stratified medicine.
description
2011 nî lūn-bûn
@nan
2011 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Quantifying factors for the success of stratified medicine.
@ast
Quantifying factors for the success of stratified medicine.
@en
Quantifying factors for the success of stratified medicine.
@nl
type
label
Quantifying factors for the success of stratified medicine.
@ast
Quantifying factors for the success of stratified medicine.
@en
Quantifying factors for the success of stratified medicine.
@nl
prefLabel
Quantifying factors for the success of stratified medicine.
@ast
Quantifying factors for the success of stratified medicine.
@en
Quantifying factors for the success of stratified medicine.
@nl
P2093
P2860
P356
P1476
Quantifying factors for the success of stratified medicine.
@en
P2093
Abhijit Mazumder
Aiden A Flynn
Alfons Lieftucht
Breon Burgess
David Swank
Ernst R Berndt
Federico Goodsaid
Judy Milloy
Mark R Trusheim
P2860
P2888
P304
P356
10.1038/NRD3557
P577
2011-10-31T00:00:00Z
P5875
P6179
1005280412