Lead success rates

G. Other applications In patients with acute myocardial infarction, coronary stenting plus ReoPro leads to a greater degree of myocardial salvage and a better clinical outcome than does fibrinolysis with a tissue plasminogen activator. As compared with placebo, early administration of ReoPro in patients with acute myocardial infarction improves coronary patency before stenting, the success rate of the... 

Given a set of drug-like molecules as query molecules, what is the success rate of a search method in returning interesting search results not only similar to the input queries but also pertinent to lead optimization and/or lead hopping ... 

Makara, G. M. and Athanasopoulos, J. (2005). Improving success rates for lead generation using affinity binding technologies. Curr. Opin. Biotechnol. 16, 666-673. 

It has been suggested that projects, in the category leading to a new business or new direchons for the company, that are lead in the early stages by specific individuals who exhibit breakthrough creativity, as measured by the Myers-Briggs creativity index (see Section C, 1.5.1), show a much higher success rate [D-16]. However, this only applies if they also work within the framework of an excellent new product development system. 

Self-organizing maps in conjunction with principal component analysis constitute a powerful approach for display and classification of multivariate data. However, this does not mean that feature selection should not be used to strengthen the classification of the data. Deletion of irrelevant features can improve the reliability of the classification because noisy variables increase the chances of false classification and decrease classification success rates on new data. Furthermore, feature selection can lead to an understanding of the essential features that play an important role in governing the behavior of the system or process under investigation. It can identify those measurements that are informative and those measurements that are uninformative. However, any approach used for feature selection should take into account the existence of redundancies in the data and be multivariate in nature to ensure identification of all relevant features. 

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