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Genetic markers associated with dihydroartemisinin–piperaquine failure in Plasmodium falciparum malaria in Cambodia: a genotype–phenotype association study
Genetic markers associated with dihydroartemisinin–piperaquine failure in Plasmodium falciparum malaria in Cambodia: a genotype–phenotype association study
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Genetic markers associated with dihydroartemisinin–piperaquine failure in Plasmodium falciparum malaria in Cambodia: a genotype–phenotype association study

Amato, Roberto, Dr ; Lim, Pharath, MD ; Miotto, Olivo, PhD ; Amaratunga, Chanaki, PhD ; Dek, Dalin, MSc ; Pearson, Richard D, PhD ; Almagro-Garcia, Jacob, MSc ; Neal, Aaron T, PhD ; Sreng, Sokunthea ; Suon, Seila, MD ; Drury, Eleanor, BSc[Hons] ; Jyothi, Dushyanth, MPhil ; Stalker, Jim, MA ; Kwiatkowski, Dominic P, FRCP ; Fairhurst, Rick M, Dr

The Lancet infectious diseases, 2017-02, Vol.17 (2), p.164-173 [Periódico revisado por pares]

United States: Elsevier Ltd

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2
adegenet: a R package for the multivariate analysis of genetic markers
adegenet: a R package for the multivariate analysis of genetic markers
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adegenet: a R package for the multivariate analysis of genetic markers

Jombart, Thibaut

Bioinformatics, 2008-06, Vol.24 (11), p.1403-1405 [Periódico revisado por pares]

Oxford: Oxford University Press

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3
Migrainomics - identifying brain and genetic markers of migraine
Migrainomics - identifying brain and genetic markers of migraine
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Migrainomics - identifying brain and genetic markers of migraine

Nyholt, Dale R ; Borsook, David ; Griffiths, Lyn R

Nature reviews. Neurology, 2017-12, Vol.13 (12), p.725-741 [Periódico revisado por pares]

England: Nature Publishing Group

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4
Association Between Behavioral, Biological, and Genetic Markers of Cardiovascular Health and MRI Markers of Brain Aging: A Cohort Study
Association Between Behavioral, Biological, and Genetic Markers of Cardiovascular Health and MRI Markers of Brain Aging: A Cohort Study
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Association Between Behavioral, Biological, and Genetic Markers of Cardiovascular Health and MRI Markers of Brain Aging: A Cohort Study

Li, Yuanjing ; Laukka, Erika J ; Dekhtyar, Serhiy ; Papenberg, Goran ; Speh, Andreja ; Fratiglioni, Laura ; Kalpouzos, Grégoria ; Qiu, Chengxuan

Neurology, 2023-01, Vol.100 (1), p.e38-e48 [Periódico revisado por pares]

United States: Lippincott Williams & Wilkins

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5
Can Genetic Markers Predict the Sporadic Form of Alzheimer’s Disease? An Updated Review on Genetic Peripheral Markers
Can Genetic Markers Predict the Sporadic Form of Alzheimer’s Disease? An Updated Review on Genetic Peripheral Markers
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Can Genetic Markers Predict the Sporadic Form of Alzheimer’s Disease? An Updated Review on Genetic Peripheral Markers

Theron, Danelda ; Hopkins, Lloyd N ; Sutherland, Heidi G ; Griffiths, Lyn R ; Fernandez, Francesca

International journal of molecular sciences, 2023-09, Vol.24 (17), p.13480 [Periódico revisado por pares]

Basel: MDPI AG

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6
Mapping migration in a songbird using high-resolution genetic markers
Mapping migration in a songbird using high-resolution genetic markers
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Mapping migration in a songbird using high-resolution genetic markers

Ruegg, Kristen C. ; Anderson, Eric C. ; Paxton, Kristina L. ; Apkenas, Vanessa ; Lao, Sirena ; Siegel, Rodney B. ; DeSante, David F. ; Moore, Frank ; Smith, Thomas B.

Molecular ecology, 2014-12, Vol.23 (23), p.5726-5739 [Periódico revisado por pares]

England: Blackwell Publishing Ltd

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7
inbreedR: an R package for the analysis of inbreeding based on genetic markers
inbreedR: an R package for the analysis of inbreeding based on genetic markers
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inbreedR: an R package for the analysis of inbreeding based on genetic markers

Stoffel, Martin A. ; Esser, Mareike ; Kardos, Marty ; Humble, Emily ; Nichols, Hazel ; David, Patrice ; Hoffman, Joseph I. ; Poisot, Timothée Poisot, Timothée

Methods in ecology and evolution, 2016-11, Vol.7 (11), p.1331-1339 [Periódico revisado por pares]

London: John Wiley & Sons, Inc

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8
Machine Learning to Predict Anti–Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers
Machine Learning to Predict Anti–Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers
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Machine Learning to Predict Anti–Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers

Guan, Yuanfang ; Zhang, Hongjiu ; Quang, Daniel ; Wang, Ziyan ; Parker, Stephen C. J. ; Pappas, Dimitrios A. ; Kremer, Joel M. ; Zhu, Fan

Arthritis & rheumatology (Hoboken, N.J.), 2019-12, Vol.71 (12), p.1987-1996 [Periódico revisado por pares]

United States: Wiley Subscription Services, Inc

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9
Genetic Markers of Toxicity From Capecitabine and Other Fluorouracil-Based Regimens: Investigation in the QUASAR2 Study, Systematic Review, and Meta-Analysis
Genetic Markers of Toxicity From Capecitabine and Other Fluorouracil-Based Regimens: Investigation in the QUASAR2 Study, Systematic Review, and Meta-Analysis
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Genetic Markers of Toxicity From Capecitabine and Other Fluorouracil-Based Regimens: Investigation in the QUASAR2 Study, Systematic Review, and Meta-Analysis

ROSMARIN, Dan ; PALLES, Claire ; NICHOLSON, George ; GONZALEZ-NEIRA, Anna ; MARTIN, Miguel ; SARGENT, Daniel ; GREEN, Erin ; MCLEOD, Howard ; ZANGER, Ulrich M ; SCHWAB, Matthias ; BRAUN, Michael ; SEYMOUR, Matthew ; CHURCH, David ; THOMPSON, Lindsay ; LACAS, Benjamin ; BOIGE, Valérie ; RIBELLES, Nuria ; AFZAL, Shoaib ; ENGHUSEN, Henrik ; JENSEN, Søren Astrup ; ETIENNE-GRIMALDI, Marie-Christine ; MILANO, Gérard ; WADELIUS, Mia ; DOMINGO, Enric ; GLIMELIUS, Bengt ; GARMO, Hans ; GUSELLA, Milena ; LECOMTE, Thierry ; LAURENT-PUIG, Pierre ; MARTINEZ-BALIBREA, Eva ; SHARMA, Rohini ; GARCIA-FONCILLAS, Jesus ; KLEIBL, Zdenek ; MOREL, Alain ; JONES, Angela ; PIGNON, Jean-Pierre ; MIDGLEY, Rachel ; KERR, David ; TOMLINSON, Ian ; JOHNSTONE, Elaine ; HAITAO WANG ; LOVE, Sharon ; JULIER, Patrick ; SCUDDER, Claire

Journal of clinical oncology, 2014-04, Vol.32 (10), p.1031-1039 [Periódico revisado por pares]

Alexandria, VA: American Society of Clinical Oncology

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10
Risk estimation model for nonalcoholic fatty liver disease in the Japanese using multiple genetic markers
Risk estimation model for nonalcoholic fatty liver disease in the Japanese using multiple genetic markers
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Risk estimation model for nonalcoholic fatty liver disease in the Japanese using multiple genetic markers

Kawaguchi, Takahisa ; Shima, Toshihide ; Mizuno, Masayuki ; Mitsumoto, Yasuhide ; Umemura, Atsushi ; Kanbara, Yoshihiro ; Tanaka, Saiyu ; Sumida, Yoshio ; Yasui, Kohichiro ; Takahashi, Meiko ; Matsuo, Keitaro ; Itoh, Yoshito ; Tokushige, Katsutoshi ; Hashimoto, Etsuko ; Kiyosawa, Kendo ; Kawaguchi, Masanori ; Itoh, Hiroyuki ; Uto, Hirofumi ; Komorizono, Yasuji ; Shirabe, Ken ; Takami, Shiro ; Takamura, Toshinari ; Kawanaka, Miwa ; Yamada, Ryo ; Matsuda, Fumihiko ; Okanoue, Takeshi Sookoian, Silvia C.

PloS one, 2018-01, Vol.13 (1), p.e0185490-e0185490 [Periódico revisado por pares]

United States: Public Library of Science

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