title: Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties.
abstract: Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity.
authors.affiliation: School of Life Sciences, Anhui University , Hefei, Anhui , China.
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Yue
authors.affiliation: School of Life Sciences, Anhui University , Hefei, Anhui , China.
authors.forename: Wenna
authors.initials: W
authors.lastname: Zhang
authors.affiliation: School of Life Sciences, Anhui University , Hefei, Anhui , China.
authors.forename: Yongming
authors.initials: Y
authors.lastname: Lu
authors.affiliation: School of Life Sciences, Anhui University , Hefei, Anhui , China.
authors.forename: Qiaoyue
authors.initials: Q
authors.lastname: Yang
authors.affiliation: School of Life Sciences, Anhui University , Hefei, Anhui , China.
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Ding
authors.affiliation: Institute of Health Sciences, Anhui University , Hefei, Anhui , China.
authors.forename: Junfeng
authors.initials: J
authors.lastname: Xia
authors.affiliation: School of Life Sciences, Anhui University , Hefei, Anhui , China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Chen
keywords: Chemical descriptors; Gene expression; Machine learning; Natural products