| 查看: 229 | 回复: 0 | ||
huqionghhkk木虫 (小有名气)
|
[求助]
急急急！！！谢谢，求一段翻译
|
|
For each compound we constructed a performance profile assigning binary ([0,1], binding data) or discrete (½−1;0;1 , functional data) values representing activity for a compound in a given assay, collecting such values across all assays into a vector x. Discretization of binding data are described elsewhere (48), and ChemBank data were handled similarly, except both high- and low-signal outlier values (56) were accepted (SI Datasets D6 and D7). All distinct performance profile vectors x for compounds were collected to set S, and Shannon entropy (H) was computed by calculating relative frequencies pðxÞ and summing frequency terms over x ∈ S: HðSÞ ¼ −ΣpðxÞ log2½pðxÞ (profile entropy). To calculate entropy for a set of profiles weighted by a specificity constraint, we first computed H separately for each subset S m of profiles sharing the same number m of nonzero profile features, then computed a weighted sum of these entropies with weighting factor w m ¼ exp½− lnð2Þm , to give H w ðSÞ ¼ Σw m HðS m Þ (weighted profile entropy). For ChemBank profiles, missing data were censored to zero before entropy calculations (SI Dataset D8). Statistical analyses, visualizations, and entropy calculations were performed in MATLAB. |
回复此楼» 猜你喜欢
310求调剂
已经有7人回复
-
考研求调剂
已经有3人回复
-
调剂
已经有4人回复
-
一志愿211，0703化学305分求调剂
已经有30人回复
-
材料工程281还有调剂机会吗
已经有14人回复
-
求调剂
已经有12人回复
-
286求调剂
已经有21人回复
-
303求调剂
已经有15人回复
-
070300化学279求调剂
已经有20人回复
-
291求调剂
已经有3人回复
