Web Tools:

    easyROC: a web-tool for ROC curve analysis
    • This web application creates ROC curves, calculates area under the curve (AUC) values and confidence intervals for the AUC values, and performs multiple comparisons for ROC curves in a user-friendly, up-to-date and comprehensive way.

    MVN: a web-tool for assessing multivariate normality
    • This application performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests. Click for TR version. Also, click for R Package version.

    geneSurv: Survival Analysis for Genomics
    • Survival analysis is often used in cancer studies. It has been shown that combination of clinical data with genomics increases predictive performance of survival analysis methods. This tool provides a wide range of survival analysis methods for genomic research, especially in cancer studies. The tool includes analysis methods including Kaplan-Meier, Cox regression, Penalized Cox regression and Random Survival Forests. It also offers methods for optimal cutoff point determination for continuous markers.

    voomDDA: Discovery of diagnostic biomarkers and classification of RNA-Seq data
    • VoomDDA is a decision support tool developed for RNA-Sequencing datasets to assist researchers in their decisions for diagnostic biomarker discovery and classification problem. VoomDDA consists both sparse and non-sparse statistical learning classifiers adapted with voom method and provides fast, accurate and sparser classification results for RNA-Seq data.

    DDNAA: Decision support system for differential diagnosis of nontraumatic acute abdomen
    • This decision support tool is developed to assist physicians in their decisions to differentially diagnose of patients with acute abdomen. DDNAA includes several diagnostic tests which combine leukocyte count and d-dimer level based on statistical learning approaches.

    MLViS: machine learning-based virtual screening tool
    • This web-tool classifies the compounds as drug-like and nondrug-like based on various trained statistical machine learning algorithms.

    R/Bioconductor Packages

    MLSeq: Machine learning interface for RNA-Seq data
    • MLSeq package provides several algorithms including support vector machines (SVM), bagging support vector machines (bagSVM), random forest (RF) and classification and regression trees (CART) to classify sequencing data. Click here for published article.