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14篇 您的检索式：作者名="HAUERT S"

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		题名	作者	年代	出处	被引量
	1	Identification and character- ization of receptor-specific peptides for siRNA delivery 显示文摘	REN Y HAUERT S LO JH	2012	ACS Nano2012，6，10：	1
	2	Replicator dynamics in optional public goods games显示文摘	Hauert C Monte S D Hofbauer J	2002	J Theor Biol2002，218，：	1
	3	Surface analysis of chemically-etched and plasma treated polyetheretherketone for biomedical applications显示文摘	Ha S W Hauert R Ernst K H	1997	Surface and Coatings Technology1997，97，：	1
	4	Dust cloud characterization and its influence on the pressure time history in silos显示文摘	Hauert F Vogl A Radandt S	1996	Process safety process1996，15，3：	1
	5	Differentiated HL450 ceils are a valid model system for the analysis of human neutrophil migration and ehemotaxis显示文摘	Hauert A B Martinelli S Marone C	2002	lnt J Bioehem Cell Biol2002，34，7：	1
	6	Volunteering as red queen mechanism for cooperation in public goods games 显示文摘	Hauert C Monte S D Hofbauer J et aI	2002	Science2002，296，5570：	1
	7	显示文摘	Hauert R Glisenti A Metin S	1995	Thin Solid Films1995，268，：	1
	8	Volunteering as Rt'd Queen Mechanisln for Cooperation in Put)lie Goods Games显示文摘	Hauert C Monte S Hofbauer J	2002	SCIENCE2002，296，：	1
	9	Volunteering as Red Queen mechanism for cooperation in public goods game显示文摘	Hauert C De Monte S Hofbauer J	2002	Science2002，296，：	1
	10	Replicator dynamics for optional public good games 显示文摘	HAUERT C DE MONTE S HOFBAUER J	2002	Theor Boil2002，218，：	1
	11	Dust cloud characterisation and its influence on the pressure-time history in silos 显示文摘	HAUERT F VOGL A RADANDT S	1996	Process Safety Progress1996，15，3：	1
	12	Influence of nitrogen doping on different properties of a-C∶H显示文摘	HAUERT R GLISENTI A MERIN S	1995	Thin Solid Films1995，268，：	1
	13	Volunteering as red queen mechanism for cooperation in public goods games显示文摘	De Monte S Hofbauer J	2002	Science2002，296，5570：	1
	14	Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment显示文摘We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments.Our work aims to decrease both the time and cost required to develop nanoparticle designs.EVONANO includes a simulator to grow tumours,extract representative scenarios,and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution.The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments.We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments.Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.	Namid R.Stillman Igor Balaz Michail-Antisthenis Tsompanas Marina Kovacevic Sepinoud Azimi Sébastien Lafond Andrew Adamatzky Sabine Hauert	2021	npj Computational Materials2021，，1：	0

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