About: Tetko Igor V.

Documentation

scienceplus.abes.fr version Bêta

scienceplus.abes.fr | explorer

À propos de : Tetko Igor V. Goto Sponge NotDistinct Permalink

Attributs	Valeurs
type	Person
name	Tetko Igor V. Tetko Igor Tetko I. V.
personal mailbox	i.tetko@gsf.de
familyName	Tetko
Given name	Igor I. V. Igor V.
is relates of	http://hub.abes.fr/acs/periodical/jcics1/1995/volume_35/issue_5/101021ci00027a006/authorship/1 http://hub.abes.fr/acs/periodical/jmcmar/1993/volume_36/issue_7/101021jm00059a003/authorship/1 http://hub.abes.fr/acs/periodical/jcics1/2001/volume_41/issue_2/101021ci000393l/authorship/1 http://hub.abes.fr/oup/periodical/bioinformatics/2004/volume_21/issue_8/101093bioinformaticsbti200/authorship/6 http://hub.abes.fr/oup/periodical/nar/2005/volume_33/issue_suppl2/101093nargki434/authorship/2 http://hub.abes.fr/acs/periodical/jcics1/2002/volume_42/issue_5/101021ci025515j/authorship/1 http://hub.abes.fr/oup/periodical/bioinformatics/2004/volume_20/issue_17/101093bioinformaticsbth376/authorship/2 http://hub.abes.fr/oup/periodical/nar/2004/volume_32/issue_18/101093nargkh894/authorship/7 http://hub.abes.fr/oup/periodical/bioinformatics/2004/volume_20/issue_5/101093bioinformaticsbtg462/authorship/2 http://hub.abes.fr/oup/periodical/bioinformatics/2005/volume_21/issue_10/101093bioinformaticsbti380/authorship/1 http://hub.abes.fr/oup/periodical/nar/2006/volume_34/issue_1/101093nargnj002/authorship/2 http://hub.abes.fr/acs/periodical/jcics1/2001/volume_41/issue_5/101021ci010368v/authorship/1 http://hub.abes.fr/acs/periodical/jcics1/1998/volume_38/issue_4/101021ci970439j/authorship/1 http://hub.abes.fr/acs/periodical/jcics1/2002/volume_42/issue_3/101021ci010379o/authorship/1 http://hub.abes.fr/oup/periodical/bioinformatics/2008/volume_24/issue_5/101093bioinformaticsbtm633/authorship/1 http://hub.abes.fr/acs/periodical/jcisd8/2008/volume_48/issue_9/101021ci800151m/authorship/1 http://hub.abes.fr/acs/periodical/jmcmar/2001/volume_44/issue_15/101021jm010858e/authorship/1 http://hub.abes.fr/acs/periodical/jmcmar/2004/volume_47/issue_23/101021jm049509l/authorship/1 http://hub.abes.fr/acs/periodical/jcisd8/2008/volume_48/issue_4/101021ci700443v/authorship/10 http://hub.abes.fr/acs/periodical/jcics1/2001/volume_41/issue_6/101021ci000392t/authorship/1 http://hub.abes.fr/acs/periodical/jcics1/2000/volume_40/issue_4/101021ci9904261/authorship/3 http://hub.abes.fr/acs/periodical/jcisd8/2006/volume_46/issue_2/101021ci0504216/authorship/1 http://hub.abes.fr/acs/periodical/ancham/1999/volume_71/issue_13/101021ac981345r/authorship/2 http://hub.abes.fr/acs/periodical/ancham/1999/volume_71/issue_13/101021ac981346j/authorship/1 http://hub.abes.fr/acs/periodical/jcics1/1996/volume_36/issue_4/101021ci950204c/authorship/1 http://hub.abes.fr/acs/periodical/jcisd8/2007/volume_47/issue_3/101021ci600493x/authorship/3 http://hub.abes.fr/acs/periodical/jcics1/1998/volume_38/issue_4/101021ci980325n/authorship/2 http://hub.abes.fr/acs/periodical/jmcmar/1994/volume_37/issue_16/101021jm00042a005/authorship/1 http://hub.abes.fr/springer/periodical/11237/1994/volume_29/issue_2/B8F45204EEE82570E053120B220A2340/authorship/3 http://hub.abes.fr/springer/periodical/11094/1996/volume_30/issue_6/B906B379BDD462D9E053120B220A6C85/authorship/4 http://hub.abes.fr/springer/periodical/11094/1992/volume_26/issue_2/B906B379BC3562D9E053120B220A6C85/authorship/2 http://hub.abes.fr/springer/periodical/11237/1994/volume_29/issue_1/B8F45204EED82570E053120B220A2340/authorship/1
is Author of	Applications of neural networks in structure-activity relationships of a small number of molecules Modified Hopfinger analysis of the phosphodiesterase inhibiting activity of flavonoids Benchmarking of Linear and Nonlinear Approaches for QuantitativeStructure−Property Relationship Studies of Metal Complexation with Ionophores The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes Éclair—a web service for unravelling species origin of sequences sampled from mixed host interfaces Neural Network Studies. 4. Introduction to Associative Neural Networks Optimization models for cancer classification: extracting gene interaction information from microarray expression data Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS2.1 Program A web portal for classification of expression data using maximal margin linear programming MIPS bacterial genomes functional annotation benchmark dataset Beyond the ‘best’ match: machine learning annotation of protein sequences by integration of different sources of information A systematic approach to infer biological relevance and biases of gene network structures Pharmaceutical Fingerprinting in Phase Space. 2.Pattern Recognition Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection Internet Software for the Calculation of the Lipophilicity and Aqueous Solubility ofChemical Compounds Application of ALOGPS 2.1 to Predictlog D Distribution Coefficient for PfizerProprietary Compounds Pharmaceutical Fingerprinting in Phase Space. 1.Construction of Phase Fingerprints Support vector machines for separation of mixed plant-pathogen EST collections based on codon usage Application of a Pruning Algorithm To Optimize Artificial Neural Networks forPharmaceutical Fingerprinting Neural Network Modeling for Estimation of Partition Coefficient Based on Atom-TypeElectrotopological State Indices Neural Network Studies. 2. Variable Selection Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices Prediction of n-Octanol/Water Partition Coefficients from PHYSPROP Database UsingArtificial Neural Networks and E-State Indices Neural Network Studies. 3. Variable Selection in the Cascade-Correlation LearningArchitecture Exhaustive QSPR Studies of a Large Diverse Set of Ionic Liquids: How Accurately CanWe Predict Melting Points? Volume Learning Algorithm Artificial Neural Networks for 3D QSAR Studies Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis Study of the structure-activity relationship in a series of physiologically active substances with a common trend of action upon the cell signaling system Use of topological indexes for prediction of the activity of 5-lipoxygenase inhibitors in a series of hydroxamates Detection of elements of structural commonality of substances with similar effects on the cell signal systems Neural network studies. 1. Comparison of overfitting and overtraining HIV-1 Reverse Transcriptase Inhibitor Design Using Artificial Neural Networks

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata