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Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Elements of Statistical Learning
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Instance-Based Learning Algorithms
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Regression by local fitting
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Piece-wise quasi-linear modeling in QSAR and analytical calibration based on linear substructures detected by genetic algorithm
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Assessing the reliability of a QSAR model's predictions
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Development and Use of Hydrophobic Surface Area (HSA) Descriptors for Computer-Assisted Quantitative Structure−Activity and Structure−Property Relationship Studies
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Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues
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Identifying Biologically Active Compound Classes Using Phenotypic Screening Data and Sampling Statistics
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Molecular Transformations as a Way of Finding and Exploiting Consistent Local QSAR
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Scalable Partitioning and Exploration of Chemical Spaces Using Geometric Hashing
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Approach to Estimation and Prediction for Normal Boiling Point (NBP) of Alkanes Based on a Novel Molecular Distance-Edge (MDE) Vector, λ
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Prediction of Physicochemical Parameters by Atomic Contributions
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Structure−Activity Relationships of the Antimalarial Agent Artemisinin. 6. The Development of Predictive In Vitro Potency Models Using CoMFA and HQSAR Methodologies
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Do Structurally Similar Molecules Have Similar Biological Activity?
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STATISTICS FOR SPATIAL DATA
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Multidimensional divide-and-conquer
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Finding groups in data: an introduction to cluster analysis
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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