Data mining approaches for genome-wide association of mood disorders (Q34073774)

30 July 2017

title

Data mining approaches for genome-wide association of mood disorders (English)

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genome-wide association study

30 July 2017

main subject

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1 reference

Mehdi Pirooznia

1

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author name string

Fayaz Seifuddin

2

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30 July 2017

Jennifer Judy

3

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30 July 2017

Pamela B Mahon

4

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30 July 2017

Bipolar Genome Study (BiGS) Consortium

5

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30 July 2017

James B Potash

6

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30 July 2017

Peter P Zandi

7

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30 July 2017

language of work or name

English

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publication date

1 April 2012

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30 July 2017

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30 July 2017

volume

22

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30 July 2017

issue

2

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30 July 2017

page(s)

55-61

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Biomedical text mining and its applications

30 July 2017

cites work

1 reference

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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Common polygenic variation contributes to risk of schizophrenia and bipolar disorder

5 July 2018

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5 July 2018

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5 July 2018

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Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder

5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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The epidemiology of mood disorders

5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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5 July 2018

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Genomewide linkage analyses of bipolar disorder: a new sample of 250 pedigrees from the National Institute of Mental Health Genetics Initiative.

5 July 2018

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5 July 2018

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5 July 2018

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Familiality of Polarity at Illness Onset in Bipolar Affective Disorder

28 October 2018

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10.1097/YPG.0B013E32834DC40D

28 October 2018

Identifiers

DOI

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30 July 2017

1 reference

30 July 2017

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