Prediction of chemical carcinogenicity by machine learning approaches

Author： Tan N. X.   Rao H. B.   Li Z. R.   Li X. Y.  

Publisher： Taylor & Francis Ltd

ISSN： 1062-936X

Source： SAR and QSAR in Environmental Research, Vol.20, Iss.1-2, 2009-01, pp. : 27-75

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

Related content

AI and SAR approaches for predicting chemical carcinogenicity: survey and status report

By Richard A.M.  

SAR and QSAR in Environmental Research, Vol. 13, Iss. 1, 2002-01 ,pp. : 1-19

Taylor & Francis Ltd

Access to resources Recommend Favorite

Application of Machine Learning for Dragline Failure Prediction

By  

E3S Web of conferences, Vol. 15, Iss. issue, 2017-04 ,pp. : 03002-03002

Edp Sciences

Access to resources Recommend Favorite

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

By Bai Yun   Sun Zhenzhong   Deng Jun   Li Lin   Long Jianyu   Li Chuan  

Sustainability, Vol. 10, Iss. 1, 2017-12 ,pp. : 85-85

MDPI

Access to resources Recommend Favorite

Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

By Ke Yinghai   Im Jungho   Park Seonyoung   Gong Huili  

Remote Sensing, Vol. 8, Iss. 3, 2016-03 ,pp. : 215-215

MDPI

Access to resources Recommend Favorite

Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China

By Liu Yangxiaoyue   Yang Yaping   Jing Wenlong   Yue Xiafang  

Remote Sensing, Vol. 10, Iss. 1, 2017-12 ,pp. : 31-31

MDPI

Access to resources Recommend Favorite