Florian Nigsch - researchr alias

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Viewing Publication 1 - 11 from 11

2016

Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput ScreeningShardul Paricharak, Adriaan P. IJzerman, Jeremy L. Jenkins, Andreas Bender, Florian Nigsch. jcisd, 56(9):1622-1630, 2016. [doi]

2012

Predicting the mechanism of phospholipidosisRobert Lowe, Hamse Y. Mussa, Florian Nigsch, Robert C. Glen, John B. O. Mitchell. jcheminf, 4:2, 2012. [doi]

Determination of minimal transcriptional signatures of compounds for target predictionFlorian Nigsch, Janna Hutz, Ben Cornett, Douglas W. Selinger, Gregory McAllister, Somnath Bandyopadhyay, Joseph Loureiro, Jeremy L. Jenkins. ejbsb, 2012:2, 2012. [doi]

2011

Activity-Aware Clustering of High Throughput Screening Data and Elucidation of Orthogonal Structure-Activity RelationshipsEugen Lounkine, Florian Nigsch, Jeremy L. Jenkins, Meir Glick. jcisd, 51(12):3158-3168, 2011. [doi]

Classifying Large Chemical Data Sets: Using A Regularized Potential Function MethodHamse Y. Mussa, Lezan Hawizy, Florian Nigsch, Robert C. Glen. jcisd, 51(1):4-14, 2011. [doi]

2008

Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log PLaura D. Hughes, David S. Palmer, Florian Nigsch, John B. O. Mitchell. jcisd, 48(1):220-232, 2008. [doi]

How To Winnow Actives from Inactives: Introducing Molecular Orthogonal Sparse Bigrams (MOSBs) and Multiclass WinnowFlorian Nigsch, John B. O. Mitchell. jcisd, 48(2):306-318, 2008. [doi]

Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance StatisticsFlorian Nigsch, Andreas Bender, Jeremy L. Jenkins, John B. O. Mitchell. jcisd, 48(12):2313-2325, 2008. [doi]

2006

k-Nearest Neighbor Algorithms and Genetic Parameter OptimizationFlorian Nigsch, Andreas Bender, Bernd van Buuren, Jos Tissen, Eduard Nigsch, John B. O. Mitchell. jcisd, 46(6):2412-2422, 2006. [doi]

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