Project description:Here, we report the development and application of a machine-learning precision method to identify cell fate determinants (CFD) that specifically reprogram GBM into induced antigen-presenting cells with DC-like functions (iDC-APC). In murine GBM models, iDC-APCs acquired DC-like morphology, regulatory gene expression profile and functions, including phagocytosis, direct presentation of endogenous antigens, and, especially, vigorous cross presentation of exogenous antigens comparable to natural DCs to prime naïve CD8+ CTLs, a hallmark DC function critical for anti-tumor immunity.
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: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:Background: Adenomyosis uteri is a chronic gynecological condition frequently coexisting with endometriosis, presenting significant diagnostic challenges due to overlapping symptoms and limitations in imaging techniques. There is a pressing need for reliable, non-invasive biomarkers to enhance diagnostic accuracy and improve patient care. Objective: This pilot study investigated the diagnostic potential of serum and urine microRNA (miRNA) profiles for adenomyosis using machine learning approaches. Methods: Serum and urine samples were collected from 59 patients undergoing surgery for chronic pelvic pain at the Endometriosis Center, RWTH Aachen University Hospital. Among them, 7 had isolated adenomyosis, 34 had histologically confirmed endometriosis, and 18 served as negative controls. miRNA profiling was conducted via next-generation sequencing. A comprehensive feature selection pipeline—including variance filtering, univariate testing, mutual information, and recursive feature elimination—was used to reduce dimensionality. Classification models (Logistic Regression, Random Forest, Support Vector Machine, and Decision Tree) were trained with cross-validation and evaluated using accuracy, precision, recall, F1 score, and ROC-AUC.