scholarly journals Development of personalized predictive models for non-IVF and IVF procedures in lesbian and single women sheds light on applicability of infertility biomarkers to women of unknown fertility status

2016 ◽  
Vol 106 (3) ◽  
pp. e173
Author(s):  
A. Santistevan ◽  
K. Hunter Cohn ◽  
J. Schnorr ◽  
F. Arredondo ◽  
B. Miller ◽  
...  
Keyword(s):  
Predictive Models ◽  
Single Women ◽  
Fertility Status

scholarly journals Does China’s Fertility Policy Induce Employment Discrimination Against Women? Evidence from a Nationwide Correspondence Experiment

2022 ◽  
Author(s):  
Xun Li ◽  
Dan Wen ◽  
Lin Ye ◽  
Jiang Yu
Keyword(s):  
Employment Discrimination ◽  
Single Women ◽  
Married Women ◽  
Fertility Status ◽  
Child Policy ◽  
Status Information ◽  
Fertility Policy

Abstract We apply a two-wave nationwide correspondence experiment to assess the effects of the two-child and three-child policies in China. Using 13,751 observations collected through this experiment, we find that the announcement of the two-child policy led to a 4.9% decrease in total interview callbacks overall, and decreases of 4.3%, 5.7%, and 5.6% for single women, those married with no children, and those married with one child, respectively. The implementation of the three-child policy led to a 10.4% decrease, but only for married women with two children. The discrimination broadly affected all women, whether they disclose marriage and fertility status information or not, as we find their callback rates decreased by 4.5% under the universal two-child policy and 6.6% after the three-child policy.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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