the purpose of this work is to develop robust and interpretable quantitative structure”activity relationship (qsar) models for assessing the aquatic toxicity of phenols using a combined set of descriptors encompassing the logp and recently developed variables (monconn-z variables). the used dataset consists of 250 chemicals with toxicity data to the ciliate tetrahymena pyriformis. for each com...

Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information. Environmental models in forest areas have been benefited by the use of LiDAR-derived information in the last years. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop those models. MLR defines the relation between t...

In this study the relationship between choosing appropriate descriptors by genetic algorithm to the Polarizability (POL), Molar Refractivity (MR) and Octanol/water Partition Coefficient (LogP) of barbiturates is studied. The chemical structures of the molecules were optimized using ab initio 6-31G basis set method and Polak-Ribiere algorithm with conjugated gradient within HyperChem 8.0 environ...

Gene Expression Programming (GEP) is a novel genetic algorithm, a highly effective, stable random searching method. We take GEP to make models of Quantitative Structure-Retention Relationship (QSRR) for a series of oxygen-containing organic compounds of GC retention index, and compare the predictive results with Artificial Neural Network (ANN) and Multiple Linear Regression (MLR). The correlati...

In this work multiple linear regression (MLR) and artificial neural network (ANN) were used to predict the retention factors of 40 basic and neutral drugs in immobilized artificial membrane liquid chromatography. Two separate models were developed for prediction of solute retention in two mobile phase compositions which were used five identical descriptors. The standard errors in ANN calculatio...

This study revisits the face-to-tongue articulatory inversion problem in speech. We compare the Multi Linear Regression method (MLR) with two more sophisticated methods based on Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs), using the same French corpus of articulatory data acquired by ElectroMagnetoGraphy. GMMs give overall results better than HMMs, but MLR does poorly. GMMs a...

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (...

In this paper, we de ne the Multilevel Relational (MLR) data model for multilevel relations with element-level labeling. This model builds upon prior work of numerous authors in this area, and integrates ideas from a number of sources. A new data-based semantics is given to the MLR data model which combines ideas from SeaView, belief-based semantics and LDV model, and has the advantages of both...

In this paper, we deene the Multilevel Rela-tional (MLR) data model for multilevel relations with element-level labeling. This model builds upon prior work of numerous authors in this area, and integrates ideas from a number of sources. A new data-based semantics is given to the MLR data model which combines ideas from SeaView, belief-based semantics and LDV model, and has the advantages of bot...

On the set of 53 trypsin inhibitors the affinity to the covalent bound ligands is modeled using linear (MLR) and non-linear (ANN) methods. Each compound is represented by 343 chemical descriptors. The hypothesis was that linear models are not sufficiently flexible to yield the best model, because in MLR (multiple regression analysis) the number of variables (descriptors) is limited by the numbe...