about
CFMDS: CUDA-based fast multidimensional scaling for genome-scale data.Identification of new p53 target microRNAs by bioinformatics and functional analysisWhole-genome cartography of p53 response elements ranked on transactivation potential.A flexible integrative approach based on random forest improves prediction of transcription factor binding sites.PhysBinder: Improving the prediction of transcription factor binding sites by flexible inclusion of biophysical properties.p53 positively regulates the expression of cancer stem cell marker CD133 in HCT116 colon cancer cells.
P2860
description
2009 nî lūn-bûn
@nan
2009 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Regression based predictor for p53 transactivation.
@ast
Regression based predictor for p53 transactivation.
@en
type
label
Regression based predictor for p53 transactivation.
@ast
Regression based predictor for p53 transactivation.
@en
prefLabel
Regression based predictor for p53 transactivation.
@ast
Regression based predictor for p53 transactivation.
@en
P2860
P356
P1433
P1476
Regression based predictor for p53 transactivation.
@en
P2093
Sivakumar Gowrisankar
P2860
P2888
P356
10.1186/1471-2105-10-215
P577
2009-07-14T00:00:00Z
P5875
P6179
1016589681