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:High-calorie diets lead hepatic steatosis and to the development of non-alcoholic fatty liver disease (NAFLD), which can evolve over many years into the inflammatory form non-alcoholic steatohepatits (NASH) posing a risk for the development of hepatocellular carcinoma (HCC). Due to the diet and the liver alteration, the axis between liver and gut is disturbed, resulting in gut microbiome alterations. Consequently, detecting these gut microbiome alterations repre-sents a promising strategy for early NASH and HCC detection. We analyzed medical parame-ters and the fecal metaproteome of 19 healthy controls, 32 NASH, and 29 HCC patients target-ing the discovery of diagnostic biomarkers. Here, NASH and HCC resulted in increased in-flammation status and shifts within the composition of the gut microbiome. Increased abun-dance of kielin/chordin, E3 ubiquitin ligase, and nucleophosmin 1 represented valuable fecal biomarkers indicating disease-related changes in the liver. Whereas a single biomarker failed to separate NASH and HCC, machine learning-based classification algorithms provided 0.86% accuracy in distinguishing between controls, NASH, and HCC. Conclusion: Fecal metaproteomics enables early detection of NASH and HCC by providing single biomarkers and ma-chine learning-based metaprotein panels.
Project description:We show that DANCE-MaP permits measurement of state-specific per-nucleotide reactivities, direct secondary structure PAIRs, and tertiary RINGs for RNA structural ensembles. Here, we demonstrate DANCE-MaP on the V. vulnificus add riboswitch.
Project description:Large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system.
Project description:CD34+ Haematopoietic stem cells were differentiated ex vivo to generate ChIP-seq data for machine learning of rules underlying open chromatin dynamics.
Project description:The RNA polymerase II core promoter is the site of convergence of the signals that lead to the initiation of transcription. Here, we perform a comparative analysis of the downstream core promoter region (DPR) in Drosophila and humans by using machine learning. These studies revealed a distinct human-specific version of the DPR and led to the use of the machine learning models for the identification of synthetic extreme DPR motifs with specificity for human transcription factors relative to Drosophila factors, and vice versa. More generally, machine learning models could be analogously used to design synthetic promoter elements with customized functional properties.
Project description:CD34+ Haematopoietic stem cells were differentiated under two ex vivo protocols to generate ATAC-seq data for machine learning of rules underlying open chromatin dynamics.
2020-07-08 | GSE136976 | GEO
Project description:visual-olfactory cross-modal associative learning in honeybee
