TY - GEN
T1 - Predicting blood-brain barrier penetration by stochastic discrimination
AU - Chen, Dechang
AU - Fang, Jianwen
AU - Yu, Jiawei
N1 - Funding Information:
I thank Professor Boubé Gado for his advice and support at Garumele, and am delighted to have had the opportunity to work together. I am grateful to the Ministère des Ensei-gnements Secondaire et Supérieur, de la Recherche et de la Technologie for allowing our research and to Inoussa Issa, Maifada Ganda, Malik Saako, and Ilya Amadou for their work and good cheer at Garumele. The ceramic analysis presented here was facilitated by Newcastle University who provided the space and facilities of the Wolfson laboratory, and by the Institut de Recherches en Sciences Humaines (Niamey) who issued a temporary export permit for the material. I am grateful to Graham Connah for unpublished data relating to the 1964 ceramics collections at Garumele, to Olivier Langlois for an advance copy of his Préhistoire Archéologie méditerrannéennes article, to Boubé Gado for relevant extracts from Maître’s unpublished report, and to two reviewers for comments on the first draft of this paper. Kevin Greene, Mark Jackson and Kat Manning offered useful discussion of pottery-related matters. Last but not least, I thank the British Institute in Eastern Africa and the British Academy for funding fieldwork and post-excavation costs respectively.
PY - 2008
Y1 - 2008
N2 - The importance of optimizing the ability to penetrate blood-brain barrier of potential drug candidates is now widely recognized. Accurate computational prediction of such a property will significantly enhance the speed of the blood-brain barrier penetration analysis and reduce the cost of drug discovery. In this paper, we present some results of our predictive model built on the stochastic discrimination, a pattern classification method that has been shown to be a useful in the literature.
AB - The importance of optimizing the ability to penetrate blood-brain barrier of potential drug candidates is now widely recognized. Accurate computational prediction of such a property will significantly enhance the speed of the blood-brain barrier penetration analysis and reduce the cost of drug discovery. In this paper, we present some results of our predictive model built on the stochastic discrimination, a pattern classification method that has been shown to be a useful in the literature.
UR - http://www.scopus.com/inward/record.url?scp=51549085816&partnerID=8YFLogxK
U2 - 10.1109/BMEI.2008.243
DO - 10.1109/BMEI.2008.243
M3 - Conference contribution
AN - SCOPUS:51549085816
SN - 9780769531182
T3 - BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
SP - 337
EP - 340
BT - BioMedical Engineering and Informatics
T2 - BioMedical Engineering and Informatics: New Development and the Future - 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
Y2 - 27 May 2008 through 30 May 2008
ER -