Project description:To explore the microRNAs associated with pathology of early Alzheimer's disease, we detected the microRNA profiles in the plasma of subjects with mild cognitive impairment due to Alzheimer's disease and gender-, age-, education-matched normal control elderly.
Project description:To explore the lncRNAs associated with pathology of early Alzheimer's disease, we detected the lncRNA profiles in the plasma of subjects with mild cognitive impairment due to Alzheimer's disease and gender-, age-, education-matched normal control elderly.
Project description:The experiment is for demonstrating the miRNA profiles in plasma exosomes derived from mild cognitive impairment and Alzheimer's disease patients and healthy donors.
Project description:The project aims to find diagnostic biomarkers for Alzheimer's disease in plasma. We applied discovery based proteomics was used to detect changes in proteins due to Alzheimer's disease using plasma samples collected from four study groups (African American Alzheimer's disease & cognitively normal, non-Hispanic-White Alzheimer's disease & cognitively normal). The data emphesizes the need for inclusion in Alzheimer's disease research.
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.