In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches
about
Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues.MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification.
P2860
In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
In Silico Prediction of Gamma- ...... -Based SVM and GBDT Approaches
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In Silico Prediction of Gamma- ...... -Based SVM and GBDT Approaches
@en
type
label
In Silico Prediction of Gamma- ...... -Based SVM and GBDT Approaches
@ast
In Silico Prediction of Gamma- ...... -Based SVM and GBDT Approaches
@en
prefLabel
In Silico Prediction of Gamma- ...... -Based SVM and GBDT Approaches
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In Silico Prediction of Gamma- ...... -Based SVM and GBDT Approaches
@en
P2860
P50
P356
P1476
In Silico Prediction of Gamma- ...... -Based SVM and GBDT Approaches
@en
P2093
P2860
P304
P356
10.1155/2016/2375268
P407
P577
2016-08-08T00:00:00Z