On robust estimation of the location parameter

Prediction of CO2 solubility in ionic liquids using machine learning methods

Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R2) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

Detecting Novel Associations in Large Data Sets

Optimization and control of energy saving side-stream extractive distillation with heat integration for separating ethyl acetate-ethanol azeotrope

Investigation on ternary system tetrahydrofuran/ethanol/water with three azeotropes separation via the combination of reactive and extractive distillation

An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures

Predictive deep learning models for environmental properties: the direct calculation of octanol–water partition coefficients from molecular graphs

A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties

A systematic modeling methodology of deep neural network-based structure-property relationship for rapid and reliable prediction on flashpoints

Insights into Ensemble Learning-based Data-Driven Model for Safety-Related Property of Chemical Substances

Zihao Wang

Papers

An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures

Predictive deep learning models for environmental properties: the direct calculation of octanol–water partition coefficients from molecular graphs

A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties

A systematic modeling methodology of deep neural network-based structure-property relationship for rapid and reliable prediction on flashpoints

Insights into Ensemble Learning-based Data-Driven Model for Safety-Related Property of Chemical Substances