簡單藥物運作機制一個藥物學數(shù)據(jù)集(SIDO A phamacology dataset)

1、簡單藥物運作機制:一個藥物學數(shù)據(jù)集(SIDO: Aphamacology dataset)數(shù)據(jù)介紹： 關鍵詞：,藥物學,預測,數(shù)據(jù)格式：數(shù)據(jù)詳細介紹：SIDO: A phamacology datasetContact: Isabelle Guyon - Submitted: 2008-09-12 02:53 - Views : 913 - EditentryAuthors: Causality workbench teamKey facts: Number of variables: 4932 (molecular descriptors) + one binarytarget variab

2、le (molecular activity).Number of examples: training 12678 + 3 test sets of 10000 examplescorresponding to unmanipulated data and 2 different types of manipulations.Variable types: binary. Some variables are real and other artificial (probes).Missing data: no.Keywords: probe.method, pharmacology, QS

3、ARPot luck task proposed: LOCANETAbstract:This is one of the datasets of the first causality challenge: causation andprediction. The goal of the challenge was to make predictions undermanipulations.SIDO (SImple Drug Operation mechanisms) contains descriptors of molecules,which have been tested again

4、st the AIDS HIV virus. The target values indicatethe molecular activity (+1 active, -1 inactive). The causal discovery task is touncover causes of molecular activity among the molecule descriptors. Thiswould help chemists in the design of new compounds, retaining activity, buthaving perhaps other desirable properties (less toxic, easier to administer).This dataset is semi-artificial: it contains both real variables and artificialvariables (probes).For the pot-luck challenge, the task proposed is to di