MALER

Machine learning (ML) methods have accelerated discoveries in biological and biomedical research, especially in the analysis of big omics data. However, biologists often lack knowledge of ML theories and programming skills to easily apply ML in their research. The lack of a one-stop web server to build, evaluate, and compare the performance of different ML models for biological data has been a major obstacle for biologists to apply ML widely.

We developed a user-friendly and easy-to-use web application called MALER (machine learning server) that helps biologists apply ML models to biological and biomedical data. MALER provides modules for (i) binary and (ii) multi-class classification, and (iii) regression and (iv) survival analysis that allow users to build, quantitatively evaluate, and compare the performance of ML models using visualizations (10, 9, 5 ML algorithms and 2, 3, 4 evaluation indices are available for classification, and regression and survival analysis, respectively). MALER supports the whole process from feature selection (5 methods), training/testing, to external validations and blind test, and provides downloadable models for future predictions using other data in the server.

In the ANALYSIS page, users can upload their omics profile dataset and the corresponding labels to build and compare their custom ML models with step by step guidance. In the PREDICT page, users can upload their ML model and a omics profile with unknown labels to predict outcomes. You can find detailed instructions on how to use MALER on the HELP page.