Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
Project description:Ginkgo biloba leaf extract (GBE) is used in traditional Chinese medicine as an herbal supplement for improving memory. Exposure of B6C3F1/N mice to GBE in a 2-year National Toxicology Program (NTP) bioassay resulted in a dose-dependent increase in hepatocellular carcinomas (HCC). To identify key microRNAs that modulate GBE-induced hepatocarcinogenesis, we compared the global miRNA expression profiles in GBE-exposed HCC (GBE-HCC) and spontaneous HCC (SPNT-HCC) with age-matched vehicle control normal livers (CNTL) from B6C3F1/N mice. The number of differentially altered miRNAs in GBE-HCC and SPNT-HCC were 74 (52 up and 22 down) and 33 (15 up and 18 down), respectively. Among the uniquely differentially altered miRNAs in GBE-HCC,, miR-31 was selected for functional validation. A potential miRNA response element (MRE) in the 3’-untranslated regions (3’-UTR) of Cdk1 mRNA was revealed by in silico analysis and confirmed by luciferase assays. In mouse hepatoma cell line HEPA-1 cells, we demonstrated an inverse correlation between miR-31 and CDK1 protein levels, but no change in Cdk1 mRNA levels, suggesting a post-transcriptional effect. Additionally, miRNA expression analysis in non-tumor liver samples from the 90-day GBE mouse study demonstrated an upregulation of miRs-411, 300, 127, 134, 409-3p, and 433-3p in GBE-exposed group compared to vehicle control group, indicating that some of these miRNAs could serve as potential biomarkers for GBE exposure or hepatocellular carcinogenesis. These data increase our understanding of miRNA-mediated epigenetic regulation of GBE-mediated hepatocellular carcinogenesis in B6C3F1/N mice.
2020-06-02 | GSE139252 | GEO
Project description:Machine Learning and honeybee waggle dance
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
Project description:CD34+ Haematopoietic stem cells were differentiated ex vivo to generate ChIP-seq data for machine learning of rules underlying open chromatin dynamics.
