Public research uploads are available. Use HTTPS to build, export and reuse models online. Submit only authorized, non-sensitive research data. De-identified molecular data can remain sensitive. The service retains unsupported Python 3.7/Django 2.1; use a controlled deployment where confidentiality or specific erasure requirements apply. Read the data handling and retention policy before uploading.

Step1: Please select an analysis mode.

Binary classification

The process of dividing new observations into distinct two categories based on the features of a set of input data.

Multiclass classfication

The process of dividing new observations into distinct at least three categories based on the features of a set of input data.

Regression

The process of exploring the relationships between dependent variables (labels) and a series of indenpendent variables (features).

Survival

The process of establishing the connection between covariates (features) and the time of events (labels) to predict the risk of future events.


Step2: Please upload the files and models.

Model:
Data:
Model: Upload a signed .maler model bundle downloaded from MALER Analysis.
File: Upload a CSV file with features in rows and samples in columns, no larger than 50 MiB.
Privacy: Submit only authorized, non-sensitive research data and compatible MALER-signed models. Do not upload direct identifiers, confidential records or sensitive patient data. Access expires after 24 hours; scheduled active-file deletion is separate from other copies. Read the data handling and retention policy. Data/model limits: 50/100 MiB.