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Multivariate Data Quality Enhancement by Ranked Imputation
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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This work presents a prediction-based data imputation technique, Rank Based Multivariate Imputation (RBMI) that operates on multivariate data. ...
Real time data is prone to inconsistencies, which exhibit negative impacts on the quality of the predictions. This mandates the need for data imputation techniques. ...
A data imputation model for genetic sequences was presented by Chen et al. [18] . The proposed Gimpute model creates pipelines to effectively perform imputation on genomes. ...
doi:10.35940/ijitee.c9027.019320
fatcat:reupha25qbhnlpiwe4odfozwzm
Generative network models identify biological mechanisms of altered structural brain connectivity in schizophrenia
[article]
2019
bioRxiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
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Spatial constraints were linked to the genetic risk for schizophrenia and general cognitive functioning, thereby providing insights into their biological basis and behavioral relevance. ...
Quality control and imputation was performed with Gimpute (31) (see Supplement for details). ...
In addition, the genetic contributions to these phenotypes can be studied with modern genetic approaches utilizing the potential of cumulative genetic risk scores. ...
doi:10.1101/604322
fatcat:om4rnp4kb5ebzlyrm47533tlam
Biologically informed risk scoring in schizophrenia based on genome-wide omics data
2020
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
For all given samples, standard quality control (QC) and imputation are performed using Gimpute pipeline ). The following QC steps were applied: 1.) ...
The same genotyping QC and imputation procedure as above was applied for GWAS MGS data. ...
doi:10.11588/heidok.00028981
fatcat:f6o4bzpuinhfdbg3pgyl76kxwm