Name
Title | ||
|---|---|---|
Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies. |
Abstract | ||
|---|---|---|
Our findings demonstrate that KNN-TN generally has improved performance in imputing the missing values of the different datasets compared to KNN-CR and KNN-EU when there is missingness due to missing at random combined with an LOD. The results shown in this study are in the field of metabolomics but this method could be applicable with any high throughput technology which has missing due to LOD. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12859-017-1547-6 | BMC Bioinformatics |
Keywords | Field | DocType |
High dimensional data,Imputation,K-nearest neighbors,Metabolomics,Missing value,Truncated normal | Truncated normal distribution,Truncation,Clustering high-dimensional data,Data set,Biology,Mean squared error,Missing data,Imputation (statistics),Bioinformatics,High throughput technology | Journal |
Volume | Issue | ISSN |
18 | 1 | 1471-2105 |
Citations | PageRank | References |
2 | 0.43 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jasmit Shah | 1 | 2 | 1.11 |
| Shesh N. Rai | 2 | 334 | 16.38 |
| Andrew P. DeFilippis | 3 | 2 | 0.77 |
| Bradford G. Hill | 4 | 2 | 0.43 |
| Aruni Bhatnagar | 5 | 2 | 0.43 |
| Guy N. Brock | 6 | 128 | 9.43 |