Project description:This collection represents whole transcriptome data for human islets that were stratified to high quality (Group 1), intermediary quality (Group 2) and poor quality (Group 3) islets (Wong WKM et al JCI Insight 2019).
Project description:The Human Liver Microsomal assay takes into account the liver-mediated drug metabolism to assess the stability of a compound in the human body. The NIH-NCATS group took a proprietary dataset of 4300 compounds with its associated HLM (in vitro half-life; unstable ≤ 30 min, stable >30 min) and used it to train a classifier.
Model Type: Machine learning model.
Model Relevance: Probability of a compound being unstable in a HLM assay.
Model Encoded by: Pauline Banye (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos31ve
Project description:To collect human tissue, blood, and fecal samples from patients suffering from Inflammatory Bowel Disease and Colorectal Cancer. The samples will be used to establish biomimetic human organ-on-a-chip technology, as well as study the role of the microbiome in the pathogenesis in human gastrointestinal diseases.
Project description:We have analyzed RNA-seq data to identify A-to-I editing sites in two groups of samples: one group isolated from human U87 cell line expressing an active ADAR3 mutant while the other isolated from U87 cell line expressing the inactive counterpart of the ADAR3 mutant. We compared these two groups of samples and identified sites whose editing levels are higher in the first group than in the second group.
Project description:We designed a custom microarray to profile the expression and used it to measure the expression of 9929 human lncRNAs manually-annotated by the GENCODE group as part of the ENCODE consortium.
Project description:We collected ovarian follicle fluids from 68 patients and assigned them to good group or bad group according to their oocyte quality. The exosomes were isolated and characterized. Exosomal microRNAs were extracted, the library was constructed and sequenced by Illumina hiseq platform. The exosomal microRNA expression was analyzed and profiled, the target genes were predicted, GO terms were enriched by GOSeq and KEGG pathway was analyzed using miranda.A total of 47 differential microRNAs was expressed significantly between good and bad group, of which 9 microRNAs were known microRNAs and 7 of them was upregulated in the bad group. In-silico analysis indicated that several of these exosomal microRNAs were involved in pathways implicated in oocyte quality.Our study suggests that exosomal microRNAs in ovarian follicle fluid are critical in maintaining the oocyte quality. Our study greatly improve our understanding of exosomal microRNAs in human ovarian follicular fluid, paving the way for further investigation on the microRNA functions in the ovarian microenvironment and the mechanism behind it.