Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
Project description:The effects of maternal microbiota on the fetal development was investigated by comparing tissues of fetuses from germ-free (GF) and normal (SPF) murine dams using RNA-seq and non-targeted metabolomics (for metabolomics data, see: https://bmcmicrobiol.biomedcentral.com/articles/10.1186/s12866-022-02457-6). For RNA-seq, two E18.5 fetuses were collected from 6 GF dams and 6 SPF dams, and transcriptomes analyzed by QuantSeq in whole intestine, brain and placenta.
Project description:Non-targeted LC-MS/MS of PPL extracts from environmental seawater samples from coral reefs collected from Maui by Dr. Megan Donahue.